This document presents a new method for diagnosing coronary artery disease (CAD) using genetic algorithm (GA) wrapped Bayes naive (BN) feature selection. The method uses a GA to generate feature subsets that are evaluated using BN classification. Over multiple iterations, the GA selects the feature subset that provides the highest accuracy. The algorithm is tested on a CAD dataset containing 13 features and achieves 85.5% classification accuracy. This performance is compared to other machine learning algorithms like SVM, MLP and C4.5 decision trees, which achieve lower accuracies of 83.5%, 83.16% and 80.85% respectively. The proposed method is also compared to other feature selection techniques like best first search and sequential floating forward search wrapped
Heart Disease Prediction using Machine Learning Algorithmijtsrd
Nowadays, Heart disease has become dangerous to a human being, it effects very badly to human body. If anyone is suffering from heart disease, then it leads to blood clotting. Heart disease prediction is very difficult task to predict in the field of medical science. Affiliation has predicted that 12 million people fail horrendously every year as a result of heart disease. In this paper, we propose a k Nearest Neighbors Algorithm KNN way to deal with improve the exactness of heart determination. We show that k Nearest Neighbors Algorithm KNN have better accuracy than random forest algorithm for viewing heart disease. The k Nearest Neighbors Algorithm give more precise and exact outcome . We have taken 13 attributes in the dataset and a target attribute, by applying machine learning we achieved 84 accuracy in the heart disease detection. Ravi Kumar Singh | Dr. A Rengarajan "Heart Disease Prediction using Machine Learning Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38358.pdf Paper Url: https://www.ijtsrd.com/computer-science/other/38358/heart-disease-prediction-using-machine-learning-algorithm/ravi-kumar-singh
Diagnosis of rheumatoid arthritis using an ensemble learning approachcsandit
Rheumatoid arthritis is one of the diseases that it
s cause is unknown yet; exploring the field of
medical data mining can be helpful in early diagnos
is and treatment of the disease. In this
study, a predictive model is suggested that diagnos
es rheumatoid arthritis. The rheumatoid
arthritis dataset was collected from 2,564 patients
referred to rheumatology clinic. For each
patient a record consists of several clinical and d
emographic features is saved. After data
analysis and pre-processing operations, three diffe
rent methods are combined to choose proper
features among all the features. Various data class
ification algorithms were applied on these
features. Among these algorithms Adaboost had the h
ighest precision. In this paper, we
proposed a new classification algorithm entitled CS
-Boost that employs Cuckoo search
algorithm for optimizing the performance of Adaboos
t algorithm. Experimental results show
that the CS-Boost algorithm enhance the accuracy of
Adaboost in predicting of Rheumatoid
Arthritis.
prediction of heart disease using machine learning algorithmsINFOGAIN PUBLICATION
The successful experiment of data mining in highly visible fields like marketing, e-business, and retail has led to its application in other sectors and industries. Healthcare is being discovered among these areas. There is an opulence of data available within the healthcare systems. However, there is a scarcity of useful analysis tool to find hidden relationships in data. This research intends to provide a detailed description of Naïve Bayes and decision tree classifier that are applied in our research particularly in the prediction of Heart Disease. Some experiment has been conducted to compare the execution of predictive data mining technique on the same dataset, and the consequence reveals that Decision Tree outperforms over Bayesian classification.
Heart Disease Prediction Using Data Mining TechniquesIJRES Journal
There are huge amounts of data in the medical industry which is not processed properly and hence cannot be used effectively in making decisions. We can use data mining techniques to mine these patterns and relationships. This research has developed a prototype Heart Disease Prediction using data mining techniques, namely Neural Network, K-Means Clustering and Frequent Item Set Generation. Using medical profiles such as age, sex, blood pressure and blood sugar it can predict the likelihood patients getting a heart disease. It enables significant knowledge, e.g. patterns, relationships between medical factors related to heart disease to be established. Performance of these techniques is compared through sensitivity, specificity and accuracy. It has been observed that Artificial Neural Networks outperform K Means clustering in all the parameters i.e. Sensitivity, Specificity and Accuracy.
Heart Disease Prediction using Machine Learning Algorithmijtsrd
Nowadays, Heart disease has become dangerous to a human being, it effects very badly to human body. If anyone is suffering from heart disease, then it leads to blood clotting. Heart disease prediction is very difficult task to predict in the field of medical science. Affiliation has predicted that 12 million people fail horrendously every year as a result of heart disease. In this paper, we propose a k Nearest Neighbors Algorithm KNN way to deal with improve the exactness of heart determination. We show that k Nearest Neighbors Algorithm KNN have better accuracy than random forest algorithm for viewing heart disease. The k Nearest Neighbors Algorithm give more precise and exact outcome . We have taken 13 attributes in the dataset and a target attribute, by applying machine learning we achieved 84 accuracy in the heart disease detection. Ravi Kumar Singh | Dr. A Rengarajan "Heart Disease Prediction using Machine Learning Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38358.pdf Paper Url: https://www.ijtsrd.com/computer-science/other/38358/heart-disease-prediction-using-machine-learning-algorithm/ravi-kumar-singh
Diagnosis of rheumatoid arthritis using an ensemble learning approachcsandit
Rheumatoid arthritis is one of the diseases that it
s cause is unknown yet; exploring the field of
medical data mining can be helpful in early diagnos
is and treatment of the disease. In this
study, a predictive model is suggested that diagnos
es rheumatoid arthritis. The rheumatoid
arthritis dataset was collected from 2,564 patients
referred to rheumatology clinic. For each
patient a record consists of several clinical and d
emographic features is saved. After data
analysis and pre-processing operations, three diffe
rent methods are combined to choose proper
features among all the features. Various data class
ification algorithms were applied on these
features. Among these algorithms Adaboost had the h
ighest precision. In this paper, we
proposed a new classification algorithm entitled CS
-Boost that employs Cuckoo search
algorithm for optimizing the performance of Adaboos
t algorithm. Experimental results show
that the CS-Boost algorithm enhance the accuracy of
Adaboost in predicting of Rheumatoid
Arthritis.
prediction of heart disease using machine learning algorithmsINFOGAIN PUBLICATION
The successful experiment of data mining in highly visible fields like marketing, e-business, and retail has led to its application in other sectors and industries. Healthcare is being discovered among these areas. There is an opulence of data available within the healthcare systems. However, there is a scarcity of useful analysis tool to find hidden relationships in data. This research intends to provide a detailed description of Naïve Bayes and decision tree classifier that are applied in our research particularly in the prediction of Heart Disease. Some experiment has been conducted to compare the execution of predictive data mining technique on the same dataset, and the consequence reveals that Decision Tree outperforms over Bayesian classification.
Heart Disease Prediction Using Data Mining TechniquesIJRES Journal
There are huge amounts of data in the medical industry which is not processed properly and hence cannot be used effectively in making decisions. We can use data mining techniques to mine these patterns and relationships. This research has developed a prototype Heart Disease Prediction using data mining techniques, namely Neural Network, K-Means Clustering and Frequent Item Set Generation. Using medical profiles such as age, sex, blood pressure and blood sugar it can predict the likelihood patients getting a heart disease. It enables significant knowledge, e.g. patterns, relationships between medical factors related to heart disease to be established. Performance of these techniques is compared through sensitivity, specificity and accuracy. It has been observed that Artificial Neural Networks outperform K Means clustering in all the parameters i.e. Sensitivity, Specificity and Accuracy.
A Survey on Heart Disease Prediction Techniquesijtsrd
Heart disease is the main reason for a huge number of deaths in the world over the last few decades and has evolved as the most life threatening disease. The health care industry is found to be rich in information. So, there is a need to discover hidden patterns and trends in them. For this purpose, data mining techniques can be applied to extract the knowledge from the large sets of data. Many researchers, in recent times have been using several machine learning techniques for predicting the heart related diseases as it can predict the disease effectively. Even though a machine learning technique proves to be effective in assisting the decision makers, still there is a scope for developing an accurate and efficient system to diagnose and predict the heart diseases thereby helping doctors with ease of work. This paper presents a survey of various techniques used for predicting heart disease and reviews their performance. G. Niranjana | Dr I. Elizabeth Shanthi "A Survey on Heart Disease Prediction Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38349.pdf Paper Url: https://www.ijtsrd.com/computer-science/other/38349/a-survey-on-heart-disease-prediction-techniques/g-niranjana
The Analysis of Performace Model Tiered Artificial Neural Network for Assessm...IJECEIAES
The assessment model of coronary heart disease is so much developed in line with the development of information technology, particularly the field of artificial intelligence. Unfortunately, the assessment models developed mostly do not use such an approach made by the clinician, that is the tiered approach. This makes the assessment process should conduct a thorough examination. This study aims to analyze the performance of a tiered model assessment. The assessment system is divided into several levels, with reference to the stages of the inspection procedure.The method used for each level is, preprocessing, building architecture artificial neural network (ANN), conduct training using the Levenberg-Marquardt algorithm and one step secant, as well as testing the system. The test results showed the influence of each level, both when the output level of the previous positive or negative, were tested back at the next level. The effect indicates that the level above gives performance improvement and or strengthens the performance at the previous level.
DATA MINING CLASSIFICATION ALGORITHMS FOR KIDNEY DISEASE PREDICTION IJCI JOURNAL
Data mining is a non-trivial process of categorizing valid, novel, potentially useful and ultimately understandable patterns in data. In terms, it accurately state as the extraction of information from a huge database. Data mining is a vital role in several applications such as business organizations, educational institutions, government sectors, health care industry, scientific and engineering. . In the health care
industry, the data mining is predominantly used for disease prediction. Enormous data mining techniques are existing for predicting diseases namely classification, clustering, association rules, summarizations, regression and etc. The main objective of this research work is to predict kidney diseases using classification algorithms such as Naïve Bayes and Support Vector Machine. This research work mainly
focused on finding the best classification algorithm based on the classification accuracy and execution time performance factors. From the experimental results it is observed that the performance of the SVM is better than the Naive Bayes classifier algorithm.
A Heart Disease Prediction Model using Logistic Regression By Cleveland DataBaseijtsrd
The early prognosis of cardiovascular diseases can aid in making decisions to lifestyle changes in high risk patients and in turn reduce their complications. Research has attempted to pinpoint the most influential factors of heart disease as well as accurately predict the overall risk using homogenous data mining techniques. Recent research has delved into amalgamating these techniques using approaches such as hybrid data mining algorithms. This paper proposes a rule based model to compare the accuracies of applying rules to the individual results of logistic regression on the Cleveland Heart Disease Database in order to present an accurate model of predicting heart disease. K. Sandhya Rani | M. Sai Chaitanya | G. Sai Kiran"A Heart Disease Prediction Model using Logistic Regression By Cleveland DataBase" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11402.pdf http://www.ijtsrd.com/computer-science/data-miining/11402/a-heart-disease-prediction-model-using-logistic-regression-by-cleveland-database/k-sandhya-rani
Psdot 14 using data mining techniques in heartZTech Proje
FINAL YEAR IEEE PROJECTS,
EMBEDDED SYSTEMS PROJECTS,
ENGINEERING PROJECTS,
MCA PROJECTS,
ROBOTICS PROJECTS,
ARM PIC BASED PROJECTS, MICRO CONTROLLER PROJECTS Z Technologies, Chennai
BLOOD TUMOR PREDICTION USING DATA MINING TECHNIQUEShiij
Healthcare systems generate a huge data collected from medical tests. Data mining is the computing
process of discovering patterns in large data sets such as medical examinations. Blood diseases are not an
exception; there are many test data can be collected from their patients. In this paper, we applied data
mining techniques to discover the relations between blood test characteristics and blood tumor in order to
predict the disease in an early stage, which can be used to enhance the curing ability. We conducted
experiments in our blood test dataset using three different data mining techniques which are association
rules, rule induction and deep learning. The goal of our experiments is to generate models that can
distinguish patients with normal blood disease from patients who have blood tumor. We evaluated our
results using different metrics applied on real data collected from Gaza European hospital in Palestine.
The final results showed that association rules could give us the relationship between blood test
characteristics and blood tumor. Also, it demonstrated that deep learning classifiers has the best ability to
predict tumor types of blood diseases with an accuracy of 79.45%. Also, rule induction gave us an
explanation of rules that describes both tumor in blood and normal hematology.
COMPARISON AND EVALUATION DATA MINING TECHNIQUES IN THE DIAGNOSIS OF HEART DI...ijcsa
Heart disease is one of the biggest health problems in the world because of high mortality and morbidity
caused by the disease. The use of data mining on medical data brought valuable and effective life
achievements and can enhance medical knowledge to make necessary decisions. Data mining plays an
important role in the field of medical science to solve health problems and diagnose ailments in critical
conditions and in normal conditions. For this reason, in this paper, data mining techniques are used to
diagnose heart disease from a dataset that includes 200 samples from different patients. Techniques used to
diagnose heart disease include Bagging, AdaBoostM1, Random Forest, Naive Bayes, RBF Network, IBK,
and NNge that all the techniques used to diagnose heart disease use Weka tool. Then these techniques are
compared to determine which is more accurate in the diagnosis of heart disease that according to the
results, it was found that the RBF Network with the accuracy of 88.2% is the most accurate classification in
the diagnosis of heart disease.
A Heart Disease Prediction Model using Logistic Regressionijtsrd
The early prognosis of cardiovascular diseases can aid in making decisions to lifestyle changes in high risk patients and in turn reduce their complications. Research has attempted to pinpoint the most influential factors of heart disease as well as accurately predict the overall risk using homogenous data mining techniques. Recent research has delved into amalgamating these techniques using approaches such as hybrid data mining algorithms. This paper proposes a rule based model to compare the accuracies of applying rules to the individual results of logistic regression on the Cleveland Heart Disease Database in order to present an accurate model of predicting heart disease. K. Sandhya Rani | M. Sai Manoj | G. Suguna Mani"A Heart Disease Prediction Model using Logistic Regression" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11401.pdf http://www.ijtsrd.com/computer-science/data-miining/11401/a-heart-disease-prediction-model-using-logistic-regression/k-sandhya-rani
A Survey on Heart Disease Prediction Techniquesijtsrd
Heart disease is the main reason for a huge number of deaths in the world over the last few decades and has evolved as the most life threatening disease. The health care industry is found to be rich in information. So, there is a need to discover hidden patterns and trends in them. For this purpose, data mining techniques can be applied to extract the knowledge from the large sets of data. Many researchers, in recent times have been using several machine learning techniques for predicting the heart related diseases as it can predict the disease effectively. Even though a machine learning technique proves to be effective in assisting the decision makers, still there is a scope for developing an accurate and efficient system to diagnose and predict the heart diseases thereby helping doctors with ease of work. This paper presents a survey of various techniques used for predicting heart disease and reviews their performance. G. Niranjana | Dr I. Elizabeth Shanthi "A Survey on Heart Disease Prediction Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38349.pdf Paper Url: https://www.ijtsrd.com/computer-science/other/38349/a-survey-on-heart-disease-prediction-techniques/g-niranjana
The Analysis of Performace Model Tiered Artificial Neural Network for Assessm...IJECEIAES
The assessment model of coronary heart disease is so much developed in line with the development of information technology, particularly the field of artificial intelligence. Unfortunately, the assessment models developed mostly do not use such an approach made by the clinician, that is the tiered approach. This makes the assessment process should conduct a thorough examination. This study aims to analyze the performance of a tiered model assessment. The assessment system is divided into several levels, with reference to the stages of the inspection procedure.The method used for each level is, preprocessing, building architecture artificial neural network (ANN), conduct training using the Levenberg-Marquardt algorithm and one step secant, as well as testing the system. The test results showed the influence of each level, both when the output level of the previous positive or negative, were tested back at the next level. The effect indicates that the level above gives performance improvement and or strengthens the performance at the previous level.
DATA MINING CLASSIFICATION ALGORITHMS FOR KIDNEY DISEASE PREDICTION IJCI JOURNAL
Data mining is a non-trivial process of categorizing valid, novel, potentially useful and ultimately understandable patterns in data. In terms, it accurately state as the extraction of information from a huge database. Data mining is a vital role in several applications such as business organizations, educational institutions, government sectors, health care industry, scientific and engineering. . In the health care
industry, the data mining is predominantly used for disease prediction. Enormous data mining techniques are existing for predicting diseases namely classification, clustering, association rules, summarizations, regression and etc. The main objective of this research work is to predict kidney diseases using classification algorithms such as Naïve Bayes and Support Vector Machine. This research work mainly
focused on finding the best classification algorithm based on the classification accuracy and execution time performance factors. From the experimental results it is observed that the performance of the SVM is better than the Naive Bayes classifier algorithm.
A Heart Disease Prediction Model using Logistic Regression By Cleveland DataBaseijtsrd
The early prognosis of cardiovascular diseases can aid in making decisions to lifestyle changes in high risk patients and in turn reduce their complications. Research has attempted to pinpoint the most influential factors of heart disease as well as accurately predict the overall risk using homogenous data mining techniques. Recent research has delved into amalgamating these techniques using approaches such as hybrid data mining algorithms. This paper proposes a rule based model to compare the accuracies of applying rules to the individual results of logistic regression on the Cleveland Heart Disease Database in order to present an accurate model of predicting heart disease. K. Sandhya Rani | M. Sai Chaitanya | G. Sai Kiran"A Heart Disease Prediction Model using Logistic Regression By Cleveland DataBase" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11402.pdf http://www.ijtsrd.com/computer-science/data-miining/11402/a-heart-disease-prediction-model-using-logistic-regression-by-cleveland-database/k-sandhya-rani
Psdot 14 using data mining techniques in heartZTech Proje
FINAL YEAR IEEE PROJECTS,
EMBEDDED SYSTEMS PROJECTS,
ENGINEERING PROJECTS,
MCA PROJECTS,
ROBOTICS PROJECTS,
ARM PIC BASED PROJECTS, MICRO CONTROLLER PROJECTS Z Technologies, Chennai
BLOOD TUMOR PREDICTION USING DATA MINING TECHNIQUEShiij
Healthcare systems generate a huge data collected from medical tests. Data mining is the computing
process of discovering patterns in large data sets such as medical examinations. Blood diseases are not an
exception; there are many test data can be collected from their patients. In this paper, we applied data
mining techniques to discover the relations between blood test characteristics and blood tumor in order to
predict the disease in an early stage, which can be used to enhance the curing ability. We conducted
experiments in our blood test dataset using three different data mining techniques which are association
rules, rule induction and deep learning. The goal of our experiments is to generate models that can
distinguish patients with normal blood disease from patients who have blood tumor. We evaluated our
results using different metrics applied on real data collected from Gaza European hospital in Palestine.
The final results showed that association rules could give us the relationship between blood test
characteristics and blood tumor. Also, it demonstrated that deep learning classifiers has the best ability to
predict tumor types of blood diseases with an accuracy of 79.45%. Also, rule induction gave us an
explanation of rules that describes both tumor in blood and normal hematology.
COMPARISON AND EVALUATION DATA MINING TECHNIQUES IN THE DIAGNOSIS OF HEART DI...ijcsa
Heart disease is one of the biggest health problems in the world because of high mortality and morbidity
caused by the disease. The use of data mining on medical data brought valuable and effective life
achievements and can enhance medical knowledge to make necessary decisions. Data mining plays an
important role in the field of medical science to solve health problems and diagnose ailments in critical
conditions and in normal conditions. For this reason, in this paper, data mining techniques are used to
diagnose heart disease from a dataset that includes 200 samples from different patients. Techniques used to
diagnose heart disease include Bagging, AdaBoostM1, Random Forest, Naive Bayes, RBF Network, IBK,
and NNge that all the techniques used to diagnose heart disease use Weka tool. Then these techniques are
compared to determine which is more accurate in the diagnosis of heart disease that according to the
results, it was found that the RBF Network with the accuracy of 88.2% is the most accurate classification in
the diagnosis of heart disease.
A Heart Disease Prediction Model using Logistic Regressionijtsrd
The early prognosis of cardiovascular diseases can aid in making decisions to lifestyle changes in high risk patients and in turn reduce their complications. Research has attempted to pinpoint the most influential factors of heart disease as well as accurately predict the overall risk using homogenous data mining techniques. Recent research has delved into amalgamating these techniques using approaches such as hybrid data mining algorithms. This paper proposes a rule based model to compare the accuracies of applying rules to the individual results of logistic regression on the Cleveland Heart Disease Database in order to present an accurate model of predicting heart disease. K. Sandhya Rani | M. Sai Manoj | G. Suguna Mani"A Heart Disease Prediction Model using Logistic Regression" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11401.pdf http://www.ijtsrd.com/computer-science/data-miining/11401/a-heart-disease-prediction-model-using-logistic-regression/k-sandhya-rani
Machine learning approach for predicting heart and diabetes diseases using da...IAESIJAI
Environmental changes and food habits affect people's health with numerous diseases in today's life. Machine learning is a technique that plays a vital role in predicting diseases from collected data. The health sector has plenty of electronic medical data, which helps this technique to diagnose various diseases quickly and accurately. There has been an improvement in accuracy in medical data analysis as data continues to grow in the medical field. Doctors may have a hard time predicting symptoms accurately. This proposed work utilized Kaggle data to predict and diagnose heart and diabetic diseases. The diseases heart and diabetes are the foremost cause of higher death rates for people. The dataset contains target features for the diagnosis of heart disease. This work finds the target variable for diabetic disease by comparing the patient's blood sugars to normal levels. Blood pressure, body mass index (BMI), and other factors diagnose these diseases and disorders. This work justifies the filter method and principal component analysis for selecting and extracting the feature. The main aim of this work is to highlight the implementation of three ensemble techniques-Adaptive boost, Extreme Gradient boosting, and Gradient boosting-as well as the emphasis placed on the accuracy of the results.
Multivariate sample similarity measure for feature selection with a resemblan...IJECEIAES
Feature selection improves the classification performance of machine learning models. It also identifies the important features and eliminates those with little significance. Furthermore, feature selection reduces the dimensionality of training and testing data points. This study proposes a feature selection method that uses a multivariate sample similarity measure. The method selects features with significant contributions using a machine-learning model. The multivariate sample similarity measure is evaluated using the University of California, Irvine heart disease dataset and compared with existing feature selection methods. The multivariate sample similarity measure is evaluated with metrics such as minimum subset selected, accuracy, F1-score, and area under the curve (AUC). The results show that the proposed method is able to diagnose chest pain, thallium scan, and major vessels scanned using X-rays with a high capability to distinguish between healthy and heart disease patients with a 99.6% accuracy.
DIAGNOSIS OF RHEUMATOID ARTHRITIS USING AN ENSEMBLE LEARNING APPROACH cscpconf
Rheumatoid arthritis is one of the diseases that its cause is unknown yet; exploring the field of
medical data mining can be helpful in early diagnosis and treatment of the disease. In this
study, a predictive model is suggested that diagnoses rheumatoid arthritis. The rheumatoid
arthritis dataset was collected from 2,564 patients referred to rheumatology clinic. For each
patient a record consists of several clinical and demographic features is saved. After data
analysis and pre-processing operations, three different methods are combined to choose proper
features among all the features. Various data classification algorithms were applied on these
features. Among these algorithms Adaboost had the highest precision. In this paper, we
proposed a new classification algorithm entitled CS-Boost that employs Cuckoo search
algorithm for optimizing the performance of Adaboost algorithm. Experimental results show
that the CS-Boost algorithm enhance the accuracy of Adaboost in predicting of Rheumatoid
Arthritis.
EVALUATING THE ACCURACY OF CLASSIFICATION ALGORITHMS FOR DETECTING HEART DISE...mlaij
The healthcare industry generates enormous amounts of complex clinical data that make the prediction of
disease detection a complicated process. In medical informatics, making effective and efficient decisions is
very important. Data Mining (DM) techniques are mainly used to identify and extract hidden patterns and
interesting knowledge to diagnose and predict diseases in medical datasets. Nowadays, heart disease is
considered one of the most important problems in the healthcare field. Therefore, early diagnosis leads to
a reduction in deaths. DM techniques have proven highly effective for predicting and diagnosing heart
diseases. This work utilizes the classification algorithms with a medical dataset of heart disease; namely,
J48, Random Forest, and Naïve Bayes to discover the accuracy of their performance. We also examine the
impact of the feature selection method. A comparative and analysis study was performed to determine the
best technique using Waikato Environment for Knowledge Analysis (Weka) software, version 3.8.6. The
performance of the utilized algorithms was evaluated using standard metrics such as accuracy, sensitivity
and specificity. The importance of using classification techniques for heart disease diagnosis has been
highlighted. We also reduced the number of attributes in the dataset, which showed a significant
improvement in prediction accuracy. The results indicate that the best algorithm for predicting heart
disease was Random Forest with an accuracy of 99.24%.
EVALUATING THE ACCURACY OF CLASSIFICATION ALGORITHMS FOR DETECTING HEART DIS...mlaij
The healthcare industry generates enormous amounts of complex clinical data that make the prediction of
disease detection a complicated process. In medical informatics, making effective and efficient decisions is
very important. Data Mining (DM) techniques are mainly used to identify and extract hidden patterns and
interesting knowledge to diagnose and predict diseases in medical datasets. Nowadays, heart disease is
considered one of the most important problems in the healthcare field. Therefore, early diagnosis leads to
a reduction in deaths. DM techniques have proven highly effective for predicting and diagnosing heart
diseases. This work utilizes the classification algorithms with a medical dataset of heart disease; namely,
J48, Random Forest, and Naïve Bayes to discover the accuracy of their performance. We also examine the
impact of the feature selection method. A comparative and analysis study was performed to determine the
best technique using Waikato Environment for Knowledge Analysis (Weka) software, version 3.8.6. The
performance of the utilized algorithms was evaluated using standard metrics such as accuracy, sensitivity
and specificity. The importance of using classification techniques for heart disease diagnosis has been
highlighted. We also reduced the number of attributes in the dataset, which showed a significant
improvement in prediction accuracy. The results indicate that the best algorithm for predicting heart
disease was Random Forest with an accuracy of 99.24%
A comprehensive study of machine learning for predicting cardiovascular disea...IJECEIAES
Artificial intelligence (AI) is simulating human intelligence processes by machines and software simulators to help humans in making accurate, informed, and fast decisions based on data analysis. The medical field can make use of such AI simulators because medical data records are enormous with many overlapping parameters. Using in-depth classification techniques and data analysis can be the first step in identifying and reducing the risk factors. In this research, we are evaluating a dataset of cardiovascular abnormalities affecting a group of potential patients. We aim to employ the help of AI simulators such as Weka to understand the effect of each parameter on the risk of suffering from cardiovascular disease (CVD). We are utilizing seven classes, such as baseline accuracy, naïve Bayes, k-nearest neighbor, decision tree, support vector machine, linear regression, and artificial neural network multilayer perceptron. The classifiers are assisted by a correlation-based filter to select the most influential attributes that may have an impact on obtaining a higher classification accuracy. Analysis of the results based on sensitivity, specificity, accuracy, and precision results from Weka and Statistical Package for Social Sciences (SPSS) is illustrated. A decision tree method (J48) demonstrated its ability to classify CVD cases with high accuracy 95.76%.
ANALYSIS OF LAND SURFACE DEFORMATION GRADIENT BY DINSAR cscpconf
The progressive development of Synthetic Aperture Radar (SAR) systems diversify the exploitation of the generated images by these systems in different applications of geoscience. Detection and monitoring surface deformations, procreated by various phenomena had benefited from this evolution and had been realized by interferometry (InSAR) and differential interferometry (DInSAR) techniques. Nevertheless, spatial and temporal decorrelations of the interferometric couples used, limit strongly the precision of analysis results by these techniques. In this context, we propose, in this work, a methodological approach of surface deformation detection and analysis by differential interferograms to show the limits of this technique according to noise quality and level. The detectability model is generated from the deformation signatures, by simulating a linear fault merged to the images couples of ERS1 / ERS2 sensors acquired in a region of the Algerian south.
4D AUTOMATIC LIP-READING FOR SPEAKER'S FACE IDENTIFCATIONcscpconf
A novel based a trajectory-guided, concatenating approach for synthesizing high-quality image real sample renders video is proposed . The lips reading automated is seeking for modeled the closest real image sample sequence preserve in the library under the data video to the HMM predicted trajectory. The object trajectory is modeled obtained by projecting the face patterns into an KDA feature space is estimated. The approach for speaker's face identification by using synthesise the identity surface of a subject face from a small sample of patterns which sparsely each the view sphere. An KDA algorithm use to the Lip-reading image is discrimination, after that work consisted of in the low dimensional for the fundamental lip features vector is reduced by using the 2D-DCT.The mouth of the set area dimensionality is ordered by a normally reduction base on the PCA to obtain the Eigen lips approach, their proposed approach by[33]. The subjective performance results of the cost function under the automatic lips reading modeled , which wasn’t illustrate the superior performance of the
method.
MOVING FROM WATERFALL TO AGILE PROCESS IN SOFTWARE ENGINEERING CAPSTONE PROJE...cscpconf
Universities offer software engineering capstone course to simulate a real world-working environment in which students can work in a team for a fixed period to deliver a quality product. The objective of the paper is to report on our experience in moving from Waterfall process to Agile process in conducting the software engineering capstone project. We present the capstone course designs for both Waterfall driven and Agile driven methodologies that highlight the structure, deliverables and assessment plans.To evaluate the improvement, we conducted a survey for two different sections taught by two different instructors to evaluate students’ experience in moving from traditional Waterfall model to Agile like process. Twentyeight students filled the survey. The survey consisted of eight multiple-choice questions and an open-ended question to collect feedback from students. The survey results show that students were able to attain hands one experience, which simulate a real world-working environment. The results also show that the Agile approach helped students to have overall better design and avoid mistakes they have made in the initial design completed in of the first phase of the capstone project. In addition, they were able to decide on their team capabilities, training needs and thus learn the required technologies earlier which is reflected on the final product quality
PROMOTING STUDENT ENGAGEMENT USING SOCIAL MEDIA TECHNOLOGIEScscpconf
Using social media in education provides learners with an informal way for communication. Informal communication tends to remove barriers and hence promotes student engagement. This paper presents our experience in using three different social media technologies in teaching software project management course. We conducted different surveys at the end of every semester to evaluate students’ satisfaction and engagement. Results show that using social media enhances students’ engagement and satisfaction. However, familiarity with the tool is an important factor for student satisfaction.
A SURVEY ON QUESTION ANSWERING SYSTEMS: THE ADVANCES OF FUZZY LOGICcscpconf
In real world computing environment with using a computer to answer questions has been a human dream since the beginning of the digital era, Question-answering systems are referred to as intelligent systems, that can be used to provide responses for the questions being asked by the user based on certain facts or rules stored in the knowledge base it can generate answers of questions asked in natural , and the first main idea of fuzzy logic was to working on the problem of computer understanding of natural language, so this survey paper provides an overview on what Question-Answering is and its system architecture and the possible relationship and
different with fuzzy logic, as well as the previous related research with respect to approaches that were followed. At the end, the survey provides an analytical discussion of the proposed QA models, along or combined with fuzzy logic and their main contributions and limitations.
DYNAMIC PHONE WARPING – A METHOD TO MEASURE THE DISTANCE BETWEEN PRONUNCIATIONS cscpconf
Human beings generate different speech waveforms while speaking the same word at different times. Also, different human beings have different accents and generate significantly varying speech waveforms for the same word. There is a need to measure the distances between various words which facilitate preparation of pronunciation dictionaries. A new algorithm called Dynamic Phone Warping (DPW) is presented in this paper. It uses dynamic programming technique for global alignment and shortest distance measurements. The DPW algorithm can be used to enhance the pronunciation dictionaries of the well-known languages like English or to build pronunciation dictionaries to the less known sparse languages. The precision measurement experiments show 88.9% accuracy.
INTELLIGENT ELECTRONIC ASSESSMENT FOR SUBJECTIVE EXAMS cscpconf
In education, the use of electronic (E) examination systems is not a novel idea, as Eexamination systems have been used to conduct objective assessments for the last few years. This research deals with randomly designed E-examinations and proposes an E-assessment system that can be used for subjective questions. This system assesses answers to subjective questions by finding a matching ratio for the keywords in instructor and student answers. The matching ratio is achieved based on semantic and document similarity. The assessment system is composed of four modules: preprocessing, keyword expansion, matching, and grading. A survey and case study were used in the research design to validate the proposed system. The examination assessment system will help instructors to save time, costs, and resources, while increasing efficiency and improving the productivity of exam setting and assessments.
TWO DISCRETE BINARY VERSIONS OF AFRICAN BUFFALO OPTIMIZATION METAHEURISTICcscpconf
African Buffalo Optimization (ABO) is one of the most recent swarms intelligence based metaheuristics. ABO algorithm is inspired by the buffalo’s behavior and lifestyle. Unfortunately, the standard ABO algorithm is proposed only for continuous optimization problems. In this paper, the authors propose two discrete binary ABO algorithms to deal with binary optimization problems. In the first version (called SBABO) they use the sigmoid function and probability model to generate binary solutions. In the second version (called LBABO) they use some logical operator to operate the binary solutions. Computational results on two knapsack problems (KP and MKP) instances show the effectiveness of the proposed algorithm and their ability to achieve good and promising solutions.
DETECTION OF ALGORITHMICALLY GENERATED MALICIOUS DOMAINcscpconf
In recent years, many malware writers have relied on Dynamic Domain Name Services (DDNS) to maintain their Command and Control (C&C) network infrastructure to ensure a persistence presence on a compromised host. Amongst the various DDNS techniques, Domain Generation Algorithm (DGA) is often perceived as the most difficult to detect using traditional methods. This paper presents an approach for detecting DGA using frequency analysis of the character distribution and the weighted scores of the domain names. The approach’s feasibility is demonstrated using a range of legitimate domains and a number of malicious algorithmicallygenerated domain names. Findings from this study show that domain names made up of English characters “a-z” achieving a weighted score of < 45 are often associated with DGA. When a weighted score of < 45 is applied to the Alexa one million list of domain names, only 15% of the domain names were treated as non-human generated.
GLOBAL MUSIC ASSET ASSURANCE DIGITAL CURRENCY: A DRM SOLUTION FOR STREAMING C...cscpconf
The amount of piracy in the streaming digital content in general and the music industry in specific is posing a real challenge to digital content owners. This paper presents a DRM solution to monetizing, tracking and controlling online streaming content cross platforms for IP enabled devices. The paper benefits from the current advances in Blockchain and cryptocurrencies. Specifically, the paper presents a Global Music Asset Assurance (GoMAA) digital currency and presents the iMediaStreams Blockchain to enable the secure dissemination and tracking of the streamed content. The proposed solution provides the data owner the ability to control the flow of information even after it has been released by creating a secure, selfinstalled, cross platform reader located on the digital content file header. The proposed system provides the content owners’ options to manage their digital information (audio, video, speech, etc.), including the tracking of the most consumed segments, once it is release. The system benefits from token distribution between the content owner (Music Bands), the content distributer (Online Radio Stations) and the content consumer(Fans) on the system blockchain.
IMPORTANCE OF VERB SUFFIX MAPPING IN DISCOURSE TRANSLATION SYSTEMcscpconf
This paper discusses the importance of verb suffix mapping in Discourse translation system. In
discourse translation, the crucial step is Anaphora resolution and generation. In Anaphora
resolution, cohesion links like pronouns are identified between portions of text. These binders
make the text cohesive by referring to nouns appearing in the previous sentences or nouns
appearing in sentences after them. In Machine Translation systems, to convert the source
language sentences into meaningful target language sentences the verb suffixes should be
changed as per the cohesion links identified. This step of translation process is emphasized in
the present paper. Specifically, the discussion is on how the verbs change according to the
subjects and anaphors. To explain the concept, English is used as the source language (SL) and
an Indian language Telugu is used as Target language (TL)
EXACT SOLUTIONS OF A FAMILY OF HIGHER-DIMENSIONAL SPACE-TIME FRACTIONAL KDV-T...cscpconf
In this paper, based on the definition of conformable fractional derivative, the functional
variable method (FVM) is proposed to seek the exact traveling wave solutions of two higherdimensional
space-time fractional KdV-type equations in mathematical physics, namely the
(3+1)-dimensional space–time fractional Zakharov-Kuznetsov (ZK) equation and the (2+1)-
dimensional space–time fractional Generalized Zakharov-Kuznetsov-Benjamin-Bona-Mahony
(GZK-BBM) equation. Some new solutions are procured and depicted. These solutions, which
contain kink-shaped, singular kink, bell-shaped soliton, singular soliton and periodic wave
solutions, have many potential applications in mathematical physics and engineering. The
simplicity and reliability of the proposed method is verified.
AUTOMATED PENETRATION TESTING: AN OVERVIEWcscpconf
The using of information technology resources is rapidly increasing in organizations,
businesses, and even governments, that led to arise various attacks, and vulnerabilities in the
field. All resources make it a must to do frequently a penetration test (PT) for the environment
and see what can the attacker gain and what is the current environment's vulnerabilities. This
paper reviews some of the automated penetration testing techniques and presents its
enhancement over the traditional manual approaches. To the best of our knowledge, it is the
first research that takes into consideration the concept of penetration testing and the standards
in the area.This research tackles the comparison between the manual and automated
penetration testing, the main tools used in penetration testing. Additionally, compares between
some methodologies used to build an automated penetration testing platform.
CLASSIFICATION OF ALZHEIMER USING fMRI DATA AND BRAIN NETWORKcscpconf
Since the mid of 1990s, functional connectivity study using fMRI (fcMRI) has drawn increasing
attention of neuroscientists and computer scientists, since it opens a new window to explore
functional network of human brain with relatively high resolution. BOLD technique provides
almost accurate state of brain. Past researches prove that neuro diseases damage the brain
network interaction, protein- protein interaction and gene-gene interaction. A number of
neurological research paper also analyse the relationship among damaged part. By
computational method especially machine learning technique we can show such classifications.
In this paper we used OASIS fMRI dataset affected with Alzheimer’s disease and normal
patient’s dataset. After proper processing the fMRI data we use the processed data to form
classifier models using SVM (Support Vector Machine), KNN (K- nearest neighbour) & Naïve
Bayes. We also compare the accuracy of our proposed method with existing methods. In future,
we will other combinations of methods for better accuracy.
VALIDATION METHOD OF FUZZY ASSOCIATION RULES BASED ON FUZZY FORMAL CONCEPT AN...cscpconf
In order to treat and analyze real datasets, fuzzy association rules have been proposed. Several
algorithms have been introduced to extract these rules. However, these algorithms suffer from
the problems of utility, redundancy and large number of extracted fuzzy association rules. The
expert will then be confronted with this huge amount of fuzzy association rules. The task of
validation becomes fastidious. In order to solve these problems, we propose a new validation
method. Our method is based on three steps. (i) We extract a generic base of non redundant
fuzzy association rules by applying EFAR-PN algorithm based on fuzzy formal concept analysis.
(ii) we categorize extracted rules into groups and (iii) we evaluate the relevance of these rules
using structural equation model.
PROBABILITY BASED CLUSTER EXPANSION OVERSAMPLING TECHNIQUE FOR IMBALANCED DATAcscpconf
In many applications of data mining, class imbalance is noticed when examples in one class are
overrepresented. Traditional classifiers result in poor accuracy of the minority class due to the
class imbalance. Further, the presence of within class imbalance where classes are composed of
multiple sub-concepts with different number of examples also affect the performance of
classifier. In this paper, we propose an oversampling technique that handles between class and
within class imbalance simultaneously and also takes into consideration the generalization
ability in data space. The proposed method is based on two steps- performing Model Based
Clustering with respect to classes to identify the sub-concepts; and then computing the
separating hyperplane based on equal posterior probability between the classes. The proposed
method is tested on 10 publicly available data sets and the result shows that the proposed
method is statistically superior to other existing oversampling methods.
CHARACTER AND IMAGE RECOGNITION FOR DATA CATALOGING IN ECOLOGICAL RESEARCHcscpconf
Data collection is an essential, but manpower intensive procedure in ecological research. An
algorithm was developed by the author which incorporated two important computer vision
techniques to automate data cataloging for butterfly measurements. Optical Character
Recognition is used for character recognition and Contour Detection is used for imageprocessing.
Proper pre-processing is first done on the images to improve accuracy. Although
there are limitations to Tesseract’s detection of certain fonts, overall, it can successfully identify
words of basic fonts. Contour detection is an advanced technique that can be utilized to
measure an image. Shapes and mathematical calculations are crucial in determining the precise
location of the points on which to draw the body and forewing lines of the butterfly. Overall,
92% accuracy were achieved by the program for the set of butterflies measured.
SOCIAL MEDIA ANALYTICS FOR SENTIMENT ANALYSIS AND EVENT DETECTION IN SMART CI...cscpconf
Smart cities utilize Internet of Things (IoT) devices and sensors to enhance the quality of the city
services including energy, transportation, health, and much more. They generate massive
volumes of structured and unstructured data on a daily basis. Also, social networks, such as
Twitter, Facebook, and Google+, are becoming a new source of real-time information in smart
cities. Social network users are acting as social sensors. These datasets so large and complex
are difficult to manage with conventional data management tools and methods. To become
valuable, this massive amount of data, known as 'big data,' needs to be processed and
comprehended to hold the promise of supporting a broad range of urban and smart cities
functions, including among others transportation, water, and energy consumption, pollution
surveillance, and smart city governance. In this work, we investigate how social media analytics
help to analyze smart city data collected from various social media sources, such as Twitter and
Facebook, to detect various events taking place in a smart city and identify the importance of
events and concerns of citizens regarding some events. A case scenario analyses the opinions of
users concerning the traffic in three largest cities in the UAE
SOCIAL NETWORK HATE SPEECH DETECTION FOR AMHARIC LANGUAGEcscpconf
The anonymity of social networks makes it attractive for hate speech to mask their criminal
activities online posing a challenge to the world and in particular Ethiopia. With this everincreasing
volume of social media data, hate speech identification becomes a challenge in
aggravating conflict between citizens of nations. The high rate of production, has become
difficult to collect, store and analyze such big data using traditional detection methods. This
paper proposed the application of apache spark in hate speech detection to reduce the
challenges. Authors developed an apache spark based model to classify Amharic Facebook
posts and comments into hate and not hate. Authors employed Random forest and Naïve Bayes
for learning and Word2Vec and TF-IDF for feature selection. Tested by 10-fold crossvalidation,
the model based on word2vec embedding performed best with 79.83%accuracy. The
proposed method achieve a promising result with unique feature of spark for big data.
GENERAL REGRESSION NEURAL NETWORK BASED POS TAGGING FOR NEPALI TEXTcscpconf
This article presents Part of Speech tagging for Nepali text using General Regression Neural
Network (GRNN). The corpus is divided into two parts viz. training and testing. The network is
trained and validated on both training and testing data. It is observed that 96.13% words are
correctly being tagged on training set whereas 74.38% words are tagged correctly on testing
data set using GRNN. The result is compared with the traditional Viterbi algorithm based on
Hidden Markov Model. Viterbi algorithm yields 97.2% and 40% classification accuracies on
training and testing data sets respectively. GRNN based POS Tagger is more consistent than the
traditional Viterbi decoding technique.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
2. 42 Computer Science & Information Technology (CS & IT)
With the increasing complexity in recent years, a large amount of information in the medical field
is stored in electronic form such as the electronic patient record. These data are stored and used
primarily for the management and analysis of the patient population. They are frequently used for
research, evaluation, planning and other purposes by various users in terms of analysis and
forecasting of the health status of individuals.
Automated medical diagnostic approaches are mainly based on a Machine Learning (ML)
algorithm. Subsequently, they are trained to learn decision characteristics of a physician for an
explicit disease and then they can be used to support physician decision making to diagnose
future patients of the same disease [1, 2].Inappropriately, there is no common model that can be
adjusted for the diagnosis of all kinds of diseases [3].
A large number of features that can surpass the number of data themselves often characterizes the
data used in ML. This problem known as "the curse of dimensionality" creates a challenge for
various ML applications for decision support. This can increase the risk of taking into account
correlated or redundant attributes which can lead to lower classification accuracy [4, 5, 6].
Therefore, the process of eliminating irrelevant features is a vital phase for designing decision
support systems with high accuracy. Therefore, the key objective of this paper is to design a FS
approach to reduce dimension of CAD dataset and to obtain higher accuracy classification rates.
The GA wrapped BN consists of a two-step process: In the first step, we generate a subset of
features, the dimension of data is reduced using the GA algorithm running in parallel with BN.
Accordingly, after the new subset feature model is obtained, a BN classifier is used to measure
feature model accuracy. A10-fold cross-validation strategy has been used for validating the
obtained model. Then, the proposed approach is also compared with four additional ML
algorithms: SVM, MLP and C4.5. Additionally, the proposed algorithm is compared with other
FS Algorithms.
The rest of the paper is planned as follows. The next section introduces a literature survey for
CAD problem. Section 3 describes CAD and it is followed by a global introduction to FS
strategies. In Section 4, The ML methods are introduced used to evaluate the accuracy of the
feature model obtained by GA wrapped BN algorithm and the proposed approach is then
explained and presented. In Section 5, experimental results are discussed and the conclusion is
presented in Sections 6.
2. RELATED WORKS
A Multiple studies have been proposed in the context of Fuzzy Logic Rule based for diagnosis of
CAD. In [8] proposed a system for detecting the risk level for heart disease. This system consists
of two phases, the first is the generation of fuzzy rules, and the second is the construction of a rule
inference engine based on rules generated. Tsipouras et al. [9] have proposed a four stage system
for decision support based on fuzzy rules: 1) construction of a decision tree from the data, 2) the
extraction of rules from the tree, 3) the transformation rules from the rough form to fuzzy one and
4 ) and the model optimization. They obtained a classification accuracy of 65%. Another work in
this context [10] have developed a fuzzy system. They extracted the rules using an extraction
method based on Rough Set Theory [11]. The rules then are selected and fuzzified, after that, they
weighted the rules using the information of support. The results showed that the system is able to
have a better prediction percentage of CAD than cardiologists and angiography. Three expert
cardiologists validate the system. Patil et al. [12] have proposed an intelligent system for
predicting heart attacks; in order to make the data ready to be analyzed they integrate them into a
data warehouse. Once the data is in the data warehouse, they built clusters using the K-means
method to build groups of similar individuals. Therefore, with the help of the algorithm MAFIA,
the frequent sequences appropriate for the preaching of heart attacks were extracted. With the use
of frequent sequences as training set and the back propagation algorithm, the neural network was
used. The results were satisfied in terms of prediction. In this study [13], the authors have used
3. Computer Science & Information Technology (CS & IT) 43
techniques of data mining to study the risk factors that contribute significantly to the prediction of
coronary artery syndromes. They assumed that the class is the diagnosis - with dichotomous
values indicating the presence or absence of disease. They applied the binary regression. The data
were taken from two hospitals of Karachi, Pakistan. For better performance of the model, a data
reduction technique such as principal component analysis ACP was applied. Fidele et al [14] used
techniques of artificial intelligence as the basis for evaluating risk factors for CAD. A two-layer
perceptron that uses the Levenberg-Marquardt algorithm and back propagation. They have shown
the efficiency of their system by applying it to the Long Beach data set.
3. CORONARY ARTERY DISEASE
CAD includes a multitude of diseases related to the heart and circulatory system. Cardiovascular
disorders are the most common coronary artery disease, which relate to the arteries of the heart,
and include, among others, angina, heart failure, myocardial infarction (heart attack) and stroke
brain (stroke) that occur when the brain receives inadequate blood supply.
Like all medical fields and to prevent cardiovascular disease, one of the possible solutions is to
make people aware of their CAD risks in advance and take preventive measures accordingly.
According to experts, early detection of CAD at the stage of angina can prevent the death if the
proper medication is given by the following. This is where the importance of developing a system
for the diagnosis of CAD to assist the physicians to prevent from such Pathology. Studies that
have been made to study risk factors for the CAD [15, 16], other studies that try to analyze the
12-lead ECG [17, 18] and 18-lead ECG [15].
Patients were evaluated using 14 features. The data set is taken from the Data Mining Repository
of the University of California, Irvine (UCI) [19]. To end with the system is tested using
Cleveland data sets. Features such as Age, sex, chest pain type, resting blood pressure, serum
cholesterol in mg/dl, fasting blood sugar, resting electrocardiographic results, maximum heart rate
achieved, exercise induced angina, ST depression, slope of the peak exercise ST segment, number
of major vessels, thal and the diagnosis of heart disease are presented.
4. FEATURE SELECTION APPROACH
FS is an active area of research and in development in various applications (indexing and retrieval
of images, genomic analysis, document analysis...). A large number of algorithms have been
proposed in the literature for unsupervised, supervised and semi-supervised FS. According to
Dash et al., [20] a selection process of attributes is usually composed of four steps illustrated in
Figure 1.
Figure 1. Feature Selection process
The generation procedure allows, in each iteration, to generate a subset of attributes that will be
evaluated in the second step of the selection procedure. This procedure of generation either can
start with an empty set of attributes or with the set of all attributes or with a subset of attributes
selected randomly. In the first two cases, attributes are added iteratively (forward selection) or
removed (Backward selection) [21]. Some other algorithms hybrid both concepts as Sequential
Forward Floating Selection technique that apply, after each step Forward, Backward steps while
the selected subset improves the evaluation function [22]. In the third case, a new subset of
Relevance of the Subset?
Yes
Validation
Generation Evaluation
Stop Criteria
Starting
Feature
Set
Subset of
Features
No
4. 44 Computer Science & Information Technology (CS & IT)
attributes is created randomly at each iteration (random generation).Genetic algorithms,
introduced by Holland in 1975 [23] are the most common methods used for random generation
[24].
According to the evaluation criteria used in the selection process of attributes, we can distinguish
between Wrapper approaches and Filter approaches. Wrapper Approaches use the classification
accuracy rate as evaluation criteria [25]. Filter Approaches use an evaluation function based on
the characteristics of the dataset, regardless of any classification algorithm, to select certain
attributes or a subset of attributes (information measures, consistency measures, dependence
measures and distance measures) [26][27].
4.1. The proposed Methodology
The random generation procedures explore randomly all 2n
subset candidates, which n is the
number of features in the database. A subset is therefore not the result of an increase or decrease
of features from the previous subset. This cannot stop the search when the evaluation function of
a subset reached a local optimum. However, the 2n
subsets candidates are not all evaluated. Thus,
a maximum number of iterations are imposed to ensure that computation time remains
reasonable.
In our proposed Methodology, we make use of GA for the generation process Figure 2. GA is an
adaptive heuristic search algorithm premised on the evolutionary ideas of natural selection and
genetic. In our use of genetic algorithms, a subset of features is encoded as a chromosome. This
group of chromosomes, i.e. population, is the search space of the algorithm. The fitness function
used to evaluate performance of each chromosome (subset of features) to measure its nearness to
solution. Accordingly, our study makes use of the BN Algorithm as a fitness function. The initial
subsets of features (chromosomes) as parents are used to produce new subsets of features using
genetic operators explicitly selection, mutation and crossover. At each iteration, the number of
chromosomes in the population is always constant with removing features having lowest fitness
value. This process is reiterated until a best subset of features is found or the maximum number of
iterations is attained [28].We propose a new GA Wrapped BN methodology for identifying
relevant features of CAD diagnosis Figure 2.
1 Split Dataset into 10 folds
2 For k=1 to 10
3 Test_DATA_all =(1 fold for BNWrapper
)
4 Train_data=(9 folds for training BNevaluator
)
5 for number_generation=1 to 40
6 Encode features as binary chromosomes
7 Generate randomly a population of 20 chromosomes
8 Evaluate accuracy of BNevaluator
generated in 7
9 Apply Binary Crossover with probability of 0.2
10 Apply Binary Mutation with probability of 0.09
11 Calculate the new Generated chromosomes Accuracy for BNevaluator
and
compare it with 8
12 Replace chromosomes with lowest fitness of 7 by best chromosomes
with highest fitness of 11
13 End for
14 Train BNWrapper
with Train_data
15 Test BNevaluator
with test_Data_all
16 Calculate accuracy for k
17 end for
18 Calculate average accuracy for 10 folds
Figure 2. The proposed GA wrapped BN algorithm
5. Computer Science & Information Technology (CS & IT) 45
In our model, we encoded features as binary strings of 1 and 0. In this pattern, 1 signifies
selection of a feature and 0 means a non-selection. The number of genetic factor in the
chromosome is equivalent to 14, which is the size of the features of CAD dataset. The population
consist of the number of chromosomes in the search space and we have chosen 20 as the
population size. Therefore, the first population (parent) is generated randomly. We make use of
BNevaluator as a fitness function, generally a fitness function evaluate the relevance of chromosome.
Accordingly, in our case the relevance of chromosomes is represented by classification accuracy
of the BNevaluator, so the aptness of a chromosome to be gathered depends on his accuracy rank
obtained from the fitness function (BNevaluator) at each iteration. For generation process of
chromosomes, we make use of three genetic operators. The selection operator depends of the
fitness function, so most relevant chromosomes are retained for each turn. Crossover operator
make a two-point substring exchange between two chromosomes to generate two new
chromosomes with probability of 0.2. In mutation Operator, the selected genetic factors are
inverted to avoid the search process not to get fixed in local maxima. With a mutation probability
of 0.09.
The proposed Algorithm make use of the three Operators, termination criteria for the selection
process is the number of generations. It stopped when the number of generation is equal to 40
Figure 2. Line 5. The proposed algorithm have two loops imbricate in each other, the first loop
Figure 2. Line 5 contains the generation process. First, the features are encoded as binary
chromosomes Figure 2. Line6 then a new population is generated randomly Figure 2. Line 7. The
generated population is evaluated using the fitness function (BNevaluator). Formerly, we apply the
Genetic Operators respectively Crossover Figure 2. Line 9 and mutation Figure 2. Line 10. The
new generated population is evaluated with BNevaluator and then compared with that generated in
Figure 2. Line 7. In the second, loop Figure 2. Line 2, the 9 folds set Figure 2. Line 4 with
optimized new features of loop Figure 2. Line 5 is used as the train set for BNevaluator. Having
trained BNwrapper algorithm with this train set, the algorithm is used to classify instances of
reserved test set Figure 2. Line 3. This process is reiterated 10 times with shifting folds
recursively to achieve an average classification accuracy.
4.2. Feature Selection Techniques used in this study
In order to evaluate the efficacy of our proposed GN wrapped BN technique, we compare our
methodology with another two FS wrapped methodologies. The two methodologies are based on
BN algorithm. The first one uses the Best First Search (BFS) as a generation technique. BFS is a
search algorithm that explores a graph by expanding the most promising node with the best score
which will be evaluated using the wrapped BN [29].In the second methodology we generate the
subset of features using the Sequential Floating Forward Search (SFFS). This technique is derived
from the sequential forward generation techniques. The principle of such techniques is to add one
or more attributes progressively. However, as they do not explore all possible subsets of attributes
and cannot backtrack during the search, so they are suboptimal. SFFS after each step Forward, it
applies Backward steps while the subset corresponding improves the efficacy of wrapped BN
[30].
4.3. Machine Learning techniques used in our study
Our proposed methodology selects the most pertinent features from CAD dataset and it produces
promising diagnosis accuracy. Nevertheless, to study the effectiveness of the selected features
with other ML algorithms. In this section, we introduce generally those algorithms.
4.3.1. Naïve Bayes
One of the Bayesian approaches is NB. All of Bayesian approaches use Bayes formula (1). The
main hypothesis of this kind of methods is independency of features. Thus, when features are
dependent on each other, this algorithm produce a low classification accuracy [31].
6. 46 Computer Science & Information Technology (CS & IT)
(1)
4.3.2. C4.5 Tree Algorithm
One of the important decision tree algorithms is C4.5 [32]. This algorithm can deal with all kinds
of data. It uses pruning techniques to increase accuracy and Gain Ratio for selecting features. For
instance, C4.5 can use a pruning algorithm such as reduce error pruning and it increases accuracy
of the algorithm. One of its parameters is M, which the minimum number of instances that a leaf
should have. The second one is C, it is threshold for confidence, and it is used for pruning
process.
4.3.3. Support Vector Machine
SVM method is a supervised ML method, used for classification. It is widely used to produce a
predicting model. For each given test input, SVM predicts which of two possible classes forms
the input, making it a non-probabilistic binary linear classifier [33]. Given a training se of
instance label pairs , i=1,……,r, where and . SVM involves the
resolution of the problem given by (2)
With (2)
In (2), are training vectors and they are mapped into a higher dimensional space by the
function . The C is the decision parameter for the error term. SVM finds a linear separating
hyper plane with the highest margin in the dimensional space. Accordingly, the solution of (2)
permit only a linear separation solution. Conversely, the use of a kernel allows nonlinear
separation using a kernel function (linear, polynomial, radial basis and sigmoid kernel). In our
study, we use the polynomial kernel.
4.3.4. Multi-Layer Perceptron
MLP is feed-forward neural networks trained with the standard back-propagation algorithm [34].
It is supervised networks, so it learns based on an input data to conclude a desired response, so
they are widely used for pattern classification. MLP contain one or two hidden layers. Obviously,
the structure of MLP consists of an input and an output layer with one or more hidden layers. And
for each node in one layer is linked to every node in the following layer with a weight of .In
MLP, the learning task occurs while the weights of nodes are updated with the use of back
propagation method and the amount of error in the output compared to the desired result. More
explicitly, error in output node j in the nth data point is represented by (3).
(3)
In the equation, d is the desired value and y is the value produced by the MLP. Then, updating the
weights (5) of the nodes based on those corrections, which minimize the entire output error, must
be assessed and this is given by (4).
(4)
(5)
Where the output of the previous neuron and is the learning rate. In our experiments, we uses
a learning rate of 0.3 and training time as 500 iterations.
7. Computer Science & Information Technology (CS & IT) 47
5. EXPERIMENTS AND RESULTS
For experimentation, we have chosen UCI CAD dataset, which is broadly accepted databases
acquired from the UCI machine learning repository. In the testing phase, the testing dataset is
given to the system to find the risk forecast of heart patients and achieved results are evaluated
with the evaluation metric accuracy [35].
Accuracy is the typically used measure to evaluate the efficacy ML method; it is used to reckon
how the test was worthy and consistent. In order to calculate these metric, we first compute some
of the terms like, True positive, True negative, False negative and False positive based on Table
1., where TP is the True positive, TN is the True negative, FN is the False negative and FP is the
False positive.
Table 1. Confusion Matrix.
Result of the
diagnostic test
Physician diagnosis
Positive Negative
Classifier
Result
Positive TP FP
Negative FN TN
The experiments are approved and for each of the proposed wrappers. We first calculated average
accuracies of the wrapper algorithms coupled with BN classifier. In addition, each wrapper
strategy produces feature subsets and we made experiments to measure effectiveness of these
features with the use of the described ML methods (Section 4.3) used for CAD diagnosis. The
results of the experiments are evaluated with average Accuracy. In order to show the relevance of
FS strategies, we involved the performance of ML classifiers without FS in Table 3. Moreover,
Table 2 delivers the list of features for CAD dataset and the selected subset of features generated
by the wrapper algorithms.
Table 2. The selected features of CAD dataset by different wrapper techniques.
N° Feature
Selected Features by FS approaches
GA
wrapped
BN
GA
wrapped
SVM
GA
wrapped
MLP
GA
wrapped
C4.5
BFS
wrapped
BN
SFFS
wrapped
BN
1 Chest pain type: cp
2 Age
3 Sex
4
Resting blood
pressure: restbps
5 Cholesterol: chol
6
Fasting blood
sugar: fbs
7
Resting
electrocardiographi
c results: restescg
8
Maximum heart
rate achieved:
thalach
8. 48 Computer Science & Information Technology (CS & IT)
9
Exercise induced
angina: exang
10
ST depression
induced by exercise
relative to rest:
oldpeak
11
The slope of the
peak exercise ST
segment: slope
12
Number of major
vessels(0-3)
colored by
fluoroscopy: ca
13 Thalium: thal
Table 3. show results of average accuracies acquiredusing10-fold cross validation. It is
recognizable from Table 3. that GA wrapper produces the most efficient feature model and
therefore BN products a major diagnosis accuracy of 85.50%. Instead, it can be examined from
Table 3. that feature selection algorithms produces a strength features compared to full dataset
(without FS). Table 3. In addition, presents that other ML algorithms have satisfactory results.
These classification performance results show that the feature model engendered with GA
wrapper approach is powerful. Generally, results of our experiments and that from literature are
presented in Figure 3. It is perceived that the proposed algorithm has the highest classification
accuracy in the literature.
Table 3. The performance evaluations of wrapper based feature selection algorithms.
Wrapper
Algorithms
Accuracy of different ML methods
BN SVM MLP C4.5
GA wrapper 85.50 83.82 79.86 78.54
BFS wrapper 83.50 80.53 80.53 78.55
SFFS wrapper 84.49 83.17 77.89 78.22
Without FS 82.50 83.17 79.20 76.57
5.1. Discussion
Generally, results of our experiments and that from literature are presented in Figure 3. It is
perceived that the proposed algorithm has the highest classification accuracy in the literature.
This study presents a GA wrapped BN classification model for diagnosis of CAD. The
experimental results clearly show the efficiency of feature model selected by GA wrapper. In
order to prove that, the proposed GA wrapper BN was compared with some other clinical systems
from the literature. The algorithm was compared also with two wrapper FS methods (BFS and
SFFS). The classification accuracies demonstrate the effectiveness of the features subset selected
by GA.
9. Computer Science & Information Technology (CS & IT) 49
57,58%
45,22%
68,75%
73,40%
73,50%
65,20%
79,75%
78,40%
80%
83%
81%
85%
84,20%
95,33%
83,33%
84,50%
84,25%
84,90%
AC C U R A CY S EN S I T I VI TY S P EC I FI TY
Anooj [8] Tsipouras [9] Abidin et al. [36]
Setiawan et al.[10] Debabrata et al.[37] Özçift et al.[38]
Figure 3. Analysis of different Systems
6. CONCLUSIONS
We have presented a new GA wrapped BN FS algorithm for the diagnosis of the heart disease.
The automatic process to generate the subset of features is an advantage of the proposed
algorithm. The proposed algorithm for CAD patients contains two steps such as: (1) generation of
a subset of features and (2) and the evaluation of the system using BN ML technique. The
experimental results demonstrate the strength of the proposed GA wrapped BN algorithm for
selecting the most relevant features for efficient diagnosis of CAD diseases. Generally automated
disease diagnosis problems need a reduction of Features space step to achieve high accuracy
performance. Consequently, the proposed algorithm is applied to CAD disease.
REFERENCES
[1] Abbasi, M.M., Kashiyarndi, S., (2006) “Clinical decision support systems: a discussion on different
methodologies used in health care”.
[2] F. Volkmar, A. Diegeler, T.,Walther, et al., (2000) “Total endoscopic coronary artery bypass
grafting”. Eur J CardiothoracSurg, vol. 17, pp. 38–45.
[3] Miller RA., (1994) “Medical diagnostic decision support systems—past, present, and future: a
threaded bibliography and brief commentary”. J Am Med Inform Assoc 1(1):8-27.
[4] W.N. Gansterer, G.F. Ecker, (2008) “on the relationship between feature selection and classification
accuracy”, Work 4 90–105.
[5] K. Polat, S. Günes, (2006) “The effect to diagnostic accuracy of decision tree classifier of fuzzy and
k-NN based weighted pre-processing methods to diagnosis of erythemato-squamous diseases”, Digital
Signal Process. 16 922–930.
[6] J. Xie, C. Wang, (2011) “Using support vector machines with a novel hybrid feature selection method
for diagnosis of erythemato-squamous diseases”, Expert Syst. Appl. 38 5809–5815.
[7] Kheireddine Merrad, (2012) “les facteurs de risque liés aux maladies cardiovasculaires”, journée
commémorative du cinquantenaire de la clinique de cardiologie du CHU Mustapha Pacha, alger.
[8] P.K. Anooj, (2012) “Clinical decision support system: Risk level prediction of heart disease using
weighted fuzzy rules”, Journal of King Saud University – Computer and Information Sciences 24, 27–
40
[9] Tsipouras, M.G., Exarchos, T.P., Fotiadis, D.I., Kotsia, A.P., Vakalis, K.V., Naka, K.K., Michalis,
L.K., (2008) “Automated diagnosis of coronary artery disease based on data mining and fuzzy
modeling”. IEEE Transactions on Information Technology in Biomedicine 12 (4), 447–458.
[10] Setiawan, N.A., Venkatachalam, P.A., Hani, A.F.M., (2009) “Diagnosis of coronary artery disease
using artificial intelligence based decision support system”. Proceedings of the International
Conference on Man-Machine Systems, BatuFerringhi, Penang.
[11] J. Komorowski and A. Ohrn, (199) “Modelling prognostic power of cardiac tests using rough sets”.
Artificial Intelligence in Medicine, vol. 15, pp. 167.
10. 50 Computer Science & Information Technology (CS & IT)
[12] Patil, S.B., Kumaraswamy, Y.S., (2009) “Intelligent and effective heart attack predic-tion system
using data mining and artificial neural network”. European Journal of Scientific Research 31 (4), 642–
656.
[13] Tahseen A. Jilani, Huda Yasin, MadihaYasin, Cemal Ardil, (2009) “Acute Coronary Syndrome
prediction Using Data Mining Techniques- an Application”, World Academy of Science, Engineering
and Technology, 35.
[14] Fidele, B., Cheeneebash, J., Gopaul, A., Goorah, S.S.D., (2009) “Artificial neural network as a
clinical decision-supporting tool to predict cardiovascular disease”. Trends in Applied Sciences
Research 4 (1), 36–46.
[15] S.F. Wung, B. Drew, (1999) “Comparison of 18-lead ECG and selected body surface potential
mapping leads in determining maximally deviated ST lead and efficacy in detecting acute myocardial
ischemia during coronary occlusion”, Journal of Electro-cardiology 32 (Suppl1) 30–37.
[16] A.V. Raygani, H. Ghaneialvar, Z. Rahimi, H. Nomani, M. Saidi, F. Bahrehmand, A. Vaisi-Raygani,
H. Tavilani, T. Pourmotabbed, (2010) “The angiotensin converting en-zyme D allele is an
independent risk factor for early onset coronary artery disease”, Clinical Biochemistry 43 (15) 1189–
1194.
[17] X. Yang, X. Ning, J. Wang, (2007) “Multifractal analysis of human synchronous 12-lead ECG signals
using multiple scale factors”, Physica A: Statistical Mechanics and its Applications 384 (2) 413–422.
[18] B.J. Drew, M.M. Pelter, E. Lee, J. Zegre, D. Schindler, K.E. Fleischmann, (2005) “Designing pre-
hospital ECG systems for acute coronary syndromes. Lessons learned from clinical trials involving
12-lead ST-segment monitoring”, Journal of Electro-cardiology 38 (Suppl.) 180–185.
[19] Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J., (1998) “UCI repository of machine learning
databases”. Department of Information and Computer Science, University California Irvine.
[20] M. Dash, H. Liu, and H. Motoda (2000) “Consistency based feature selection”. In Proceedings of the
4th International Conference on Knowledge Discovery and Data Mining ’ICKDDM00’, pages 98–
109.
[21] GUYON, Isabelle et ELISSEEFF, André., (2003) “An introduction to variable and feature selection”.
The Journal of Machine Learning Research, vol. 3, p. 1157-1182.
[22] PUDIL, Pavel, NOVOVIČOVÁ, Jana, et KITTLER, Josef., (1994) “Floating search methods in
feature selection”. Pattern recognition letters, vol. 15, no 11, p. 1119-1125.
[23] J.H. Holland., (1975) “Adaptation in natural and artificial systems”. Ann Arbor, the University of
Michigan Press.
[24] D. Goldberg., (1989) “Genetic algorithms in search, optimization, and machine learning”. Addison-
Wisley Editions.
[25] R.Kohavi and G. John., (1997) “Wrappers for feature subset selection”. Artificial Intelligence, pages
273–324.
[26] X. He, D. Cai, and P. Niyogi., (2005) “Laplacian score for feature selection”. In Proceedings of the
Advances in Neural Information Processing Systems ’NIPS 05’, pages 507–514, Vancouver, Canada.
[27] L. Talavera., (1999) “Feature selection as a preprocessing step for hierarchical clustering”. In
Proceedings of the 16th International Conference on Machine Learning ’ICML 99’, pages 433–443,
Bled, Slovenia.
[28] H. Yan, J. Zheng, Y. Jiang, C. Peng, S. Xiao, (2008) “Selecting critical clinical features for heart
diseases diagnosis with a real-coded genetic algorithm”, Appl. Soft Comput. 8 1105–1111.
[29] DECHTER, Rinaet PEARL, Judea, (1985) “Generalized best-first search strategies and the optimality
of A*”. Journal of the ACM (JACM), vol. 32, no 3, p. 505-536.
[30] PUDIL, Pavel, NOVOVIČOVÁ, Jana, et KITTLER, Josef. (1994) “Floating search methods in
feature selection”. Pattern recognition letters, vol. 15, no 11, p. 1119-1125.
[31] LEWIS, David D., (1998) “Naive (Bayes) at forty: The independence assumption in information
retrieval”. Machine learning: ECML-98. Springer Berlin Heidelberg, p. 4-15.
[32] QUINLAN, J. Ross., (1996) “Bagging, boosting, and C4. 5”. Proceedings of the National Conference
on Artificial Intelligence. p. 725-730.
[33] J.C.Platt, (1998) “Sequential minimal optimization: A fast algorithm for training support vector
machines”. Technical report MSR-TR-98-14, Microsoft Research.
[34] Haykin, S., (1999). “Neural network: A comprehensive foundation”. Upper Saddle River, NJ:
Prentice Hall.
[35] Zhu, W., Zeng, N., Wang, N., (2010) “Sensitivity, specificity, accuracy, associated confidence
interval and roc analysis with practical SAS implementations”. NESUG Proceedings: Health Care and
Life Sciences, Baltimore, Maryland.
11. Computer Science & Information Technology (CS & IT) 51
[36] ABIDIN, Basir, DOM, Rosma Mohd, RAHMAN, A. Rashid A., et al. (2009) “Use of fuzzy neural
network to predict coronary heart disease in a Malaysian sample”. BAYKARA, N. A. et
MASTORAKIS, N. E. (ed.). WSEAS International Conference. Proceedings. Mathematics and
Computers in Science and Engineering. World Scientific and Engineering Academy and Society.
[37] PAL, Debabrata, MANDANA, K. M., PAL, Sarbajit, et al. (2012) “Fuzzy expert system approach for
coronary artery disease screening using clinical parameters”. Knowledge-Based Systems.
[38] ÖZÇIFT, Akın et GÜLTEN, Arif. (2009) “Genetic algorithm wrapped Bayesian network feature
selection applied to differential diagnosis of erythemato-squamous diseases”. Digital Signal
Processing.
Authors
Sidahmed MOKEDDEM was born in Mostaganem, Algeria, in 1989. He received
the Master degree in computer engineering and informatics from the University of
Oran, Oran, Algeria, in 2010. He is currently working toward the Ph.D. degree in
computer science at the Department of computer science, University of Oran.
He is with the Laboratory of Oran LIO, Department of Computer Science,
University of Oran. His current research interests include medical data mining,
decision support systems in healthcare, and biomedical applications and bio-
informatiques.
Baghdad ATMANI received his Ph.D. degree in computer science from the
University of Oran (Algeria), in 2007. His interest field is Data Mining and Machine
Learning Tools. His research is based on Knowledge Representation, Knowledge-
based Systems and CBR, Data and Information Integration and Modelling, Data
Mining Algorithms, Expert Systems and Decision Support Systems.
His research are guided and evaluated through various applications in the field of
control systems, scheduling, production, maintenance, information retrieval,
simulation, data integration and spatial data mining.
Mostefa MOKADDEM received the Engineer Diploma (1985), and the Magister
(2008) in Computer Sciences at the University of Oran, Algeria. His is Assistant
Professor at the Computer Science Department of University of Oran.
His current research interests are related with modeling methodologies,
parallel/distributed simulation, Service Oriented Architecture, Knowledge
Representation, Knowledge-based Systems, Data and Information Integration, Expert
Systems and Decision Support Systems. His researches are guided and evaluated
through various applications particularly in epidemic modeling and spatial data
mining.