The document describes a predictive data mining algorithm for medical diagnosis that uses support vector machine (SVM) and random forest (RF) algorithms. It analyzes diabetes, kidney, and liver disease databases using these techniques. The proposed algorithm applies SVM and RF to the datasets and achieves high prediction accuracies of 99.35%, 99.37%, and 99.14% for diabetes, kidney, and liver diseases respectively. It also compares the performance of SVM and RF based on metrics like precision, recall, accuracy, and execution time.
Diabetes is a disease which is rapidly increasing all over the world. It occurs when pancreas does not produce sufficient insulin, or body can not sufficiently use insulin it produces. Diabetes person has increase blood glucose in the body. One of the major problem diabetic patients suffers from is the Diabetic Retinopathy (DR) and blindness. Since the number of diabetes patients is continuously increasing, it increases the data as well.
Prediction of Heart Disease using Machine Learning Algorithms: A Surveyrahulmonikasharma
According to recent survey by WHO organisation 17.5 million people dead each year. It will increase to 75 million in the year 2030[1].Medical professionals working in the field of heart disease have their own limitation, they can predict chance of heart attack up to 67% accuracy[2], with the current epidemic scenario doctors need a support system for more accurate prediction of heart disease. Machine learning algorithm and deep learning opens new door opportunities for precise predication of heart attack. Paper provideslot information about state of art methods in Machine learning and deep learning. An analytical comparison has been provided to help new researches’ working in this field.
This is a small presentation on my project , diabetes prediction using R language.The method used is knn(K nearest neighbour). it the basic Machine learning algorithm.
Diabetes Prediction Using Machine Learningjagan477830
Our proposed system aims at Predicting the number of Diabetes patients and eliminating the risk of False Negatives Drastically.
In proposed System, we use Random forest, Decision tree, Logistic Regression and Gradient Boosting Classifier to classify the Patients who are affected with Diabetes or not.
Random Forest and Decision Tree are the algorithms which can be used for both classification and regression.
The dataset is classified into trained and test dataset where the data can be trained individually, these algorithms are very easy to implement as well as very efficient in producing better results and can able to process large amount of data.
Even for large dataset these algorithms are extremely fast and can able to give accuracy of about over 90%.
Diabetes is a disease which is rapidly increasing all over the world. It occurs when pancreas does not produce sufficient insulin, or body can not sufficiently use insulin it produces. Diabetes person has increase blood glucose in the body. One of the major problem diabetic patients suffers from is the Diabetic Retinopathy (DR) and blindness. Since the number of diabetes patients is continuously increasing, it increases the data as well.
Prediction of Heart Disease using Machine Learning Algorithms: A Surveyrahulmonikasharma
According to recent survey by WHO organisation 17.5 million people dead each year. It will increase to 75 million in the year 2030[1].Medical professionals working in the field of heart disease have their own limitation, they can predict chance of heart attack up to 67% accuracy[2], with the current epidemic scenario doctors need a support system for more accurate prediction of heart disease. Machine learning algorithm and deep learning opens new door opportunities for precise predication of heart attack. Paper provideslot information about state of art methods in Machine learning and deep learning. An analytical comparison has been provided to help new researches’ working in this field.
This is a small presentation on my project , diabetes prediction using R language.The method used is knn(K nearest neighbour). it the basic Machine learning algorithm.
Diabetes Prediction Using Machine Learningjagan477830
Our proposed system aims at Predicting the number of Diabetes patients and eliminating the risk of False Negatives Drastically.
In proposed System, we use Random forest, Decision tree, Logistic Regression and Gradient Boosting Classifier to classify the Patients who are affected with Diabetes or not.
Random Forest and Decision Tree are the algorithms which can be used for both classification and regression.
The dataset is classified into trained and test dataset where the data can be trained individually, these algorithms are very easy to implement as well as very efficient in producing better results and can able to process large amount of data.
Even for large dataset these algorithms are extremely fast and can able to give accuracy of about over 90%.
Machine Learning for Disease PredictionMustafa Oğuz
A great application field of machine learning is predicting diseases. This presentation introduces what is preventable diseases and deaths. Then examines three diverse papers to explain what has been done in the field and how the technology works. Finishes with future possibilities and enablers of the disease prediction technology.
PREDICTION OF DIABETES MELLITUS USING MACHINE LEARNING TECHNIQUESIAEME Publication
Diabetes mellitus is a common disease caused by a set of metabolic ailments
where the sugar stages over drawn-out period is very high. It touches diverse organs
of the human body which therefore harm a huge number of the body's system, in
precise the blood strains and nerves. Early prediction in such disease can be exact
and save human life. To achieve the goal, this research work mainly discovers
numerous factors associated to this disease using machine learning techniques.
Machine learning methods provide effectual outcome to extract knowledge by building
predicting models from diagnostic medical datasets together from the diabetic
patients. Quarrying knowledge from such data can be valuable to predict diabetic
patients. In this research, six popular used machine learning techniques, namely
Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), C4.5 Decision
Tree (DT), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) are
compared in order to get outstanding machine learning techniques to forecast diabetic
mellitus. Our new outcome shows that Support Vector Machine (SVM) achieved
higher accuracy compared to other machine learning techniques.
We are predicting Heart Disease by Taking 14 Medical Parameters as an inputs through 2 data Minning Techniques(Decision Tree(Faster) And KNN neighbour Algorithms(Slower)).
And Visualizing The dataset.If the output 1 then it means Higher Chances of getting Heart Attack ,if 0 then it means Less chances of Heart Attack.
Artificial intelligence (AI) is a fast-growing field and its applications to diabetes, a global pandemic, can reform the approach to diagnosis and management of this chronic condition. Principles of machine learning have been used to build algorithms to support predictive models for the risk of developing diabetes or its consequent complications. Digital therapeutics have proven to be an established intervention for lifestyle therapy in the management of diabetes. Patients are increasingly being empowered for self-management of diabetes, and both patients and health care professionals are benefitting from clinical decision support. AI allows a continuous and burden-free remote monitoring of the patient's symptoms and biomarkers. Further, social media and online communities enhance patient engagement in diabetes care. Technical advances have helped to optimize resource use in diabetes. Together, these intelligent technical reforms have produced better glycemic control with reductions in fasting and postprandial glucose levels, glucose excursions, and glycosylated hemoglobin. AI will introduce a paradigm shift in diabetes care from conventional management strategies to building targeted data-driven precision care.
Predictive Analytics and Machine Learning for Healthcare - DiabetesDr Purnendu Sekhar Das
Machine Learning on clinical datasets to predict the risk of chronic disease conditions like Type 2 Diabetes mellitus beforehand; as well as predicting outcomes like hospital readmission using EMR RWE data.
This describes the techniques that are used for prediction of heart diseases using the concept of data mining.It states about IHPDS(Intelligent Heart Disease Prediction System)
Data Science Deep Roots in Healthcare IndustryDinesh V
Data Science transforms the healthcare industry with impeccable solutions that can improve patient care through EHRs, medical imaging, drug discovery, predictive medicines and genetics and genomics.
Existing model uses structured data to predict the patients of either high risk or low risk.
But for a complex disease, structured data is not a good way to describe the disease.
We propose a new convolutional neural network (CNN)-based multimodal disease risk prediction algorithm using structured and unstructured data from hospital.
In this paper, we mainly focus on the risk prediction of cerebral infarction.
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%
Machine Learning for Disease PredictionMustafa Oğuz
A great application field of machine learning is predicting diseases. This presentation introduces what is preventable diseases and deaths. Then examines three diverse papers to explain what has been done in the field and how the technology works. Finishes with future possibilities and enablers of the disease prediction technology.
PREDICTION OF DIABETES MELLITUS USING MACHINE LEARNING TECHNIQUESIAEME Publication
Diabetes mellitus is a common disease caused by a set of metabolic ailments
where the sugar stages over drawn-out period is very high. It touches diverse organs
of the human body which therefore harm a huge number of the body's system, in
precise the blood strains and nerves. Early prediction in such disease can be exact
and save human life. To achieve the goal, this research work mainly discovers
numerous factors associated to this disease using machine learning techniques.
Machine learning methods provide effectual outcome to extract knowledge by building
predicting models from diagnostic medical datasets together from the diabetic
patients. Quarrying knowledge from such data can be valuable to predict diabetic
patients. In this research, six popular used machine learning techniques, namely
Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), C4.5 Decision
Tree (DT), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) are
compared in order to get outstanding machine learning techniques to forecast diabetic
mellitus. Our new outcome shows that Support Vector Machine (SVM) achieved
higher accuracy compared to other machine learning techniques.
We are predicting Heart Disease by Taking 14 Medical Parameters as an inputs through 2 data Minning Techniques(Decision Tree(Faster) And KNN neighbour Algorithms(Slower)).
And Visualizing The dataset.If the output 1 then it means Higher Chances of getting Heart Attack ,if 0 then it means Less chances of Heart Attack.
Artificial intelligence (AI) is a fast-growing field and its applications to diabetes, a global pandemic, can reform the approach to diagnosis and management of this chronic condition. Principles of machine learning have been used to build algorithms to support predictive models for the risk of developing diabetes or its consequent complications. Digital therapeutics have proven to be an established intervention for lifestyle therapy in the management of diabetes. Patients are increasingly being empowered for self-management of diabetes, and both patients and health care professionals are benefitting from clinical decision support. AI allows a continuous and burden-free remote monitoring of the patient's symptoms and biomarkers. Further, social media and online communities enhance patient engagement in diabetes care. Technical advances have helped to optimize resource use in diabetes. Together, these intelligent technical reforms have produced better glycemic control with reductions in fasting and postprandial glucose levels, glucose excursions, and glycosylated hemoglobin. AI will introduce a paradigm shift in diabetes care from conventional management strategies to building targeted data-driven precision care.
Predictive Analytics and Machine Learning for Healthcare - DiabetesDr Purnendu Sekhar Das
Machine Learning on clinical datasets to predict the risk of chronic disease conditions like Type 2 Diabetes mellitus beforehand; as well as predicting outcomes like hospital readmission using EMR RWE data.
This describes the techniques that are used for prediction of heart diseases using the concept of data mining.It states about IHPDS(Intelligent Heart Disease Prediction System)
Data Science Deep Roots in Healthcare IndustryDinesh V
Data Science transforms the healthcare industry with impeccable solutions that can improve patient care through EHRs, medical imaging, drug discovery, predictive medicines and genetics and genomics.
Existing model uses structured data to predict the patients of either high risk or low risk.
But for a complex disease, structured data is not a good way to describe the disease.
We propose a new convolutional neural network (CNN)-based multimodal disease risk prediction algorithm using structured and unstructured data from hospital.
In this paper, we mainly focus on the risk prediction of cerebral infarction.
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%
Chronic Kidney Disease prediction is one of the most important issues in healthcare analytics. The most interesting and challenging tasks in day to day life is prediction in medical field. In this paper, we employ some machine learning techniques for predicting the chronic kidney disease using clinical data. We use three machine learning algorithms such as Decision Tree(DT) algorithm, Naive Bayesian (NB) algorithm. The performance of the above models are compared with each other in order to select the best classifier in predicting the chronic kidney disease for given dataset.
Prognosis of Cardiac Disease using Data Mining Techniques A Comprehensive Surveyijtsrd
The Healthcare exchange generally clinical diagnosis is ended commonly by doctor's knowledge and practice. Computer Aided Decision Support System plays a major task in the medical field. Data mining provides the methodology and technology to modify these rises of data into valuable data for decision making. By utilizing data mining techniques it requires less time for the prediction of the diseases with more accuracy. Among the expanding research on coronary diseases predicting system, it has happened significant to classifications the exploration results and gives readers with a layout of the current coronary diseases forecast strategies in every discussion. Data mining tools can respond to exchange addresses that expectedly being used much time over riding to decide. In this paper we study different papers in which at least one algorithm of data mining used for the prediction of coronary diseases. As of the study it is observed that Naïve Bayes Technique increase the accuracy of the coronary diseases prediction system. The commonly used techniques for Heart Disease Prediction and their complexities are outlined in this paper. D. Haripriya | Dr. M. Lovelin Ponn Felciah "Prognosis of Cardiac Disease using Data Mining Techniques: A Comprehensive Survey" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26605.pdfPaper URL: https://www.ijtsrd.com/computer-science/data-miining/26605/prognosis-of-cardiac-disease-using-data-mining-techniques-a-comprehensive-survey/d-haripriya
A comparative analysis of classification techniques on medical data setseSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Propose a Enhanced Framework for Prediction of Heart DiseaseIJERA Editor
Heart disease diagnosis requires more experience and it is a complex task. The Heart MRI, ECG and Stress Test etc are the numbers of medical tests are prescribed by the doctor for examining the heart disease and it is the way of tradition in the prediction of heart disease. Today world, the hidden information of the huge amount of health care data is contained by the health care industry. The effective decisions are made by means of this hidden information. For appropriate results, the advanced data mining techniques with the information which is based on the computer are used. In any empirical sciences, for the inference and categorisation, the new mathematical techniques to be used called Artificial neural networks (ANNs) it also be used to the modelling of the real neural networks. Acting, Wanting, knowing, remembering, perceiving, thinking and inferring are the nature of mental phenomena and these can be understand by using the theory of ANN. The problem of probability and induction can be arised for the inference and classification because these are the powerful instruments of ANN. In this paper, the classification techniques like Naive Bayes Classification algorithm and Artificial Neural Networks are used to classify the attributes in the given data set. The attribute filtering techniques like PCA (Principle Component Analysis) filtering and Information Gain Attribute Subset Evaluation technique for feature selection in the given data set to predict the heart disease symptoms. A new framework is proposed which is based on the above techniques, the framework will take the input dataset and fed into the feature selection techniques block, which selects any one techniques that gives the least number of attributes and then classification task is done using two algorithms, the same attributes that are selected by two classification task is taken for the prediction of heart disease. This framework consumes the time for predicting the symptoms of heart disease which make the user to know the important attributes based on the proposed framework.
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
As we know that health care industry is completely based on assumptions, which after get tested and verified via various tests and patient have to be depend on the doctors knowledge on that topic . so we made a system that uses data mining techniques to predict the health of a person based on various medical test results. so we can predict the health of that person based on that analysis performed by the system.The system currently design only for heart issues, for that we had used Statlog (Heart) Data Set from UCI Machine Learning Repository it includes attributes like age, sex, chest pain type, cholesterol, sugar, outcomes,etc.for training the system. we only need to passed few general inputs in order to generate the prediction and the prediction results from all algorithms are they merged together by calculating there mean value that value shows the actual outcome of the prediction process which entirely works in background
An Approach for Disease Data Classification Using Fuzzy Support Vector MachineIOSRJECE
: Data Mining has great scope in the field of medicine. In this article we introduced one new fuzzy approach for prediction of hepatitis disease. Many researchers have proposed the use of K-nearest neighbor (KNN) for diabetes disease prediction. Some have proposed a different approach by using K-means clustering for reprocessing and then using KNN for classification. In our approach Naive Bayes classifier is used to clean the data. Finally, the classification is done using Fuzzy SVM algorithm. Hepatitis diseases data set is used to test our method. We are able to obtain model more precise than any others available in the literature. The Fuzzy SVM approach produced better result than KNN with Fuzzy c-meansand Fuzzy KNN with Fuzzy c-means. Theintroduction of Fuzzy Support Vector Machine algorithm certainly has a positive effect on the outcome of hepatitis disease. This fuzzy SVM model led to remarkable increase in classification accuracy
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.
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AN ALGORITHM FOR PREDICTIVE DATA MINING APPROACH IN MEDICAL DIAGNOSIS
1. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 1, February 2018
DOI:10.5121/ijcsit.2018.10102 11
AN ALGORITHM FOR PREDICTIVE DATA
MINING APPROACH IN MEDICAL DIAGNOSIS
Shakuntala Jatav1
and Vivek Sharma2
1
M.Tech Scholar, Department of CSE, TIT College, Bhopal
2
Professor, Department of CSE, TIT College, Bhopal
ABSTRACT
The Healthcare industry contains big and complex data that may be required in order to discover
fascinating pattern of diseases & makes effective decisions with the help of different machine learning
techniques. Advanced data mining techniques are used to discover knowledge in database and for medical
research. This paper has analyzed prediction systems for Diabetes, Kidney and Liver disease using more
number of input attributes. The data mining classification techniques, namely Support Vector
Machine(SVM) and Random Forest (RF) are analyzed on Diabetes, Kidney and Liver disease database.
The performance of these techniques is compared, based on precision, recall, accuracy, f_measure as well
as time. As a result of study the proposed algorithm is designed using SVM and RF algorithm and the
experimental result shows the accuracy of 99.35%, 99.37 and 99.14 on diabetes, kidney and liver disease
respectively.
KEYWORDS
Data Mining, Clinical Decision Support System, Disease Prediction, Classification, SVM, RF.
1. INTRODUCTION
Computational health informatics is rising research topic that involving varied sciences like
biomedical, medical, nursing, data technology, computer science, and statistics [1]. Data mining
techniques are applied to predict the effectiveness of huge and complex clinical data in order to
diagnose disease and extract information to suggest effective medical assistance [2]. In bioscience,
doctor’s facilities introduced different knowledge frameworks with plenty of data to manage
medical insurance and patient information however unfortunately, knowledge don't seem to be
mined to find hidden data for effective decision [2][3].
Clinical test outcomes are often created on the basis of doctor’s perception and experience instead
of on the knowledge enrich data masked within the database and generally this procedure prompts
unintended predispositions, doctor’s experience might not be capable to diagnose it accurately that
affects the disease diagnosis system [2][3]. In aid sector, the term data mining will mean to
research the clinical data to predict patient’s health status. Therefore discovering fascinating
pattern from healthcare information, different data mining techniques are applied with statistical
analysis, machine learning and database technology.
Predictive systems are the systems that are wont to predict some outcome on the basis of some
pattern recognition, as shown in figure 1. Disease detection is that the method by which patient’s
diagnosis is performed on the basis of symptoms analyzed which may causes difficulty while
predicting disease affect [4]. As an example, fever itself could be a symptom of the many disorders
that doesn’t tell the healthcare professional what exactly the disease is. Because the results or
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opinion vary from one physician to a different, there's a requirement to help a medical physician
which will have similar opinion certainly symptoms and disorders [5]. This may be done by
analyzing the information generated by medical data or medical records.
Figure 1: A Typical Health Informatics Processing
As a result, the new information is often compared with previous records and optimistic diagnosis
is often done. Predictive medical diagnosis could be a net application which is able to predict a
selected disorder on the basis of symptoms and supply diagnosis for same disorder which is able to
be detected by rule. Healthcare professionals use their previous data and insights to reach a
particular decision regarding any disease or disorder [6]. Within the similar manner, this paper
proposes different classification techniques for diagnosis by using generic disease datasets. This
paper brings into limelight all the benefits and drawbacks of using the various data mining
techniques for the prediction of diseases. It conjointly accounts for the prediction rate for various
techniques therefore, bringing out the comparison between each of them [7]-[10].
Data mining has been successfully used in knowledge discovery for predictive purposes to make
more active and accurate decision [11]. The main focus of the paper is on classification as well as
clustering techniques. In clustering process such as K-means, EM, Fuzzy c-means, etc, data is
partitioned into sets of clusters or sub-classes [12].Machine learning techniques such as KNN,
SVM, Naïve bayes etc, can be used to classify different objects on the basis of a training set of data
whose outcome value is known.
Clustering Techniques
The clustering process divides the data into cluster groups or subclasses. We used four clustering
algorithms, namely K-Means, EM, PAM, Fuzzy C-Means [12]. The K-Means classification
algorithm works by partitioning n observations in k-subclasses defined by centroids, where k is
chosen before the algorithm begins. K-Means and EM are two iterative algorithms. EM
(expectation-maximization) is a statistical model that depends on the unobserved latent variables to
estimate the maximum likelihood parameters. Partitioning around medoids (MAP) is similar to K-
means that partitioning is based on the K-medoids method, which divides data into a number of
disjoint clusters [12]. In fuzzy clustering, data elements can belong to multiple clusters. This is also
called soft clustering.
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Classification Techniques
Machine learning based classification techniques can be used to classify various objects based on a
series of training data whose result value is known. In this study four classification algorithms are
used: KNN, SVM, Naive Bayes and C5.0. In the nearest neighbor KN, the object is classified by
the majority of its neighbors, with the object being assigned to the most commonly used class
among its nearest neighbors. In SVM (Support Vector Machines), data is first converted into a set
of points and then classified into classes that can be separated linearly. The Naive Bayes model
calculates the probability of a set of data that can belong to a class using the Bayes rule. The C5.0
algorithm is a decision tree that recursively separates observations in branches to form a tree to
improve prediction accuracy. It is an improved version of the C4.5 and ID3 algorithms [10]. It also
provides the powerful gain method to increase the accuracy of this classification algorithm [11].
2. RELATED WORK
In [1] neural networks, decision tree and naïve bayes machine learning approach is used to
diagnose heart disease. For optimized feature selection genetic algorithm is used and obtained an
accuracy of 100%, 99.62 and 90.74 respectively.
In [2], performed a prediction of heart disease detection using neural network with genetic
algorithm based feature extraction. Back propagation based neural network weight optimization by
Genetic algorithm is designed and obtained the 89% accuracy of prediction of heart disease.
In [3], author developed a heart disease prediction system using an approach of ANN with LVQ
and achieved accuracy of about 80%, sensitivity of about 85% sensitivity as well as specificity of
about 70%.
In [4] proposed a heart disease diagnosis is proposed using lazy data mining approach with data
reduction strategies i.e. principal component analysis is used to get category association rules. The
result analysis shows that J4.8 has 10.26 enhancement as well as 8.6% enhancement over naïve
bayes.
In [5] presented a heart disease prediction system using data mining approach with two additional
features i.e. obesity and smoking to boost the prediction rate. Neural networks, Decision trees and
Naive Bayes was used in for predicting heart disease with an accuracy of 99.25%, 94.44% and
96.66% respectively.
A web based application has introduced in [6] using Naïve Bayesian algorithm which took
symptoms from user and gave the diagnosis result to the user or patient. In [7] Association rule
mining technique was used for diagnosis of diabetes. The authors concluded that the data mining
techniques when used appropriately increases the computation and also the classification
performance. These rules have the potential to improve the expert system and to make better
clinical decision making. For predicting diabetes disease on weka tool, author in research work [8]
had presented a comparison between Naïve bayes algorithm and decision tree algorithm and
achieved system accuracy of about 79.56% and 76.96% respectively.
In [9] Decision Tree, Naive Bayes, and NBTree algorithms is used for liver disease detection with
10 features. The result analysis with respect to accuracy NBTree algorithm has the highest
accuracy whereas with respect to computational time Naive Bayes algorithm performs better.
In [11] author performed a comparative analysis on clustering and classification algorithms. The
result analysis shows that the classification is better than clustering algorithms with an accuracy of
about 81%.
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In [12] author proposed classification with clustering technique i.e. KNN with FCM clustering and
F-KNN with FCM clustering. It is clear that the Fuzzy KNN with Fuzzy c-means model produced
the better result than the KNN with Fuzzy c-means model on both PIMA and Liver-disorder
datasets. It is also clear that the use of Fuzzy c-means clustering algorithm for preprocessing of
datasets improved the result in terms of classification accuracy and speed by reducing the number
of tuples from the original datasets. From experiment, it is been found that KNN with Fuzzy c-
means have accuracy of 97.02 and Fuzzy KNN with Fuzzy c-means have accuracy of 99.25 on
PIMA dataset whereas on Liver disorder KNN with Fuzzy c-means have accuracy of 96.13 and
Fuzzy KNN with Fuzzy c-means with accuracy of 98.95.
In [13] performed the chronic disease prediction by using data mining approach such as Naïve
Bayes, Decision tree, Support Vector Machine (SVM) and Artificial Neural Networks (ANN) for
the diagnosis of diabetes and heart disease. The result analysis shows that SVM gives highest
accuracy of 95.556% in case of heart disease and Naïve bayes gives accuracy of 73.588% in case
of diabetes. Table I gives the comparative analysis of different existing techniques for heart, liver
and diabetes diseases.
Table 1: Comparative Analysis of Different Techniques in terms of Accuracy
Name of Author Technique Accuracy
Bhatla et al.
Neural Network, Naïve Bayes
and Decision Tree for heart
disease detection.
Neural networks = 100 %
Decision tree= 99.62 %
Naïve Bayes 90.74 %
Amin et al.
Optimized Neural Network for
heart disease detection.
Training data was = 89%
Validation data = 96.2%.
Chen et al.
ANN based heart disease
detection.
ANN = 80%.
Dangare et al.
Decision trees, Neural networks
and Naive Bayes for heart
disease detection.
Neural Networks = 99.25%
Naive Bayes = 94.44 %
Decision Tree =96.66 %
Iyer et al.
Decision tree and Naïve bayes
algorithm for diabetes detection.
Decision Tree =76.96%
Naïve Bayes= 79.56%
Uma Ojha and
Savita Goel
Decision Tree, SVM, FCM
Decision tree (C5.0) =81%
SVM =81%
FCM = 37%
Chetty et al.
KNN and F-KNN for diabetes
and liver disease detection.
KNN = 97.02%
F-KNN = 99.25% for diabetes data.
KNN = 96.13%
F-KNN =98.95% for liver disease data.
Kumari Deepika
and Dr. S. Seema
Naïve Bayes, Decision tree,
Support Vector Machine (SVM)
and Artificial Neural Networks
(ANN) for diabetes and heart
disease detection.
SVM = 95.556% (heart disease)
Naïve Bayes = 73.588% (diabetes).
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3. METHODOLOGY
3.1 Proposed Methodology
One of the interesting and important subjects among researchers in the field of medical and
computer science is diagnosing illness by considering the features that have the most impact on
recognitions. The subject discusses a new concept which is called Medical Data Mining (MDM).
Indeed, data mining methods use different ways such as classification and clustering to classify
diseases and their symptoms which are helpful for diagnosing.
A disease diagnosis system is created in order to predict different diseases such as diabetes,
kidney disease as well as liver disease, etc. System’s workflow is discussed below:
Step 1: Through the proposed application user (doctor, patient, physician etc.) can input the
attribute values of disease and send it to the decision support system for analysis.
Step 2: At decision support system, dataset of different diseases are loaded and apply data mining
algorithms to train dataset. Requested user inputs are collected and processed on server to predict
the diagnosis result.
Step 3: For analyzing healthcare data, major steps of data mining approaches like preprocess data,
replace missing values, feature selection, machine learning and make decision are applied on train
dataset. On the decision support system end different classification algorithms would be executed
on train dataset and ready to classify the test dataset.
In the proposed algorithm Support Vector Machine and Random Forest is used to give clustering
level for different subspaces. The voting model will ensemble all these results and output the final
classification result. Finally, the predicted results will be compared with true labels of the testing
phase.
Figure 2: Proposed Model
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Support Vector Machine
Support vector machine is a machine learning approach that can be used as classifier as well as
for regression. SVM classifies the data into different classes by finding hyperplane (line) which
separates training data into classes. SVM does not overfit the data and gives best classification
performance in terms of precision and accuracy.
SVM does not make any strong assumptions on data. It shows more efficiency for correct
classification of the future data. SVM is classified into 2 categories i.e. Linear and non-Linear. In
linear approach, training data is separated by line i.e. hyperplane.
Random Forest
Random Forest algorithm is capable of performing each classification and regression tasks. the
fundamental principle of RF is that a group of weak learner’s come together to create a robust
learner. Random forest rule uses bagging approach to form the bunch of decision trees with
random set of the information. The model is trained few times on random sample of the dataset to
attain best prediction performance from the RF rule. In this ensemble technique of learning, the
output of all decision trees within the RF is combined to form a final prediction. The final
prediction of the RF rule is derived after polling the results of every decision tree.
Suppose there are N cases within the training set. Then these N samples are taken randomly
however with replacement. These samples are training set for growth of tree. If m < M is specific.
The simplest split of this m is employed to separate the node. The value of m is constant whereas
growing the forest.
3.2 Proposed Algorithm
Input: D {Clininal data};
Output: Label {Disease Label};
Patient’s Label{Normal, Disease}
Step1: Pre-processing and data cleansing
Step2: For each instance in D, do
Find feature vector (V)
Step 3: For each V do
Data clustering using FCM and split data in two halves and classify data using SVM and RF
algorithm
Step 4: Determine the total class label
Find
True_positive (TP)
True_negative (TN)
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False_positive (FP)
False_negative (FN)
Step 5: Find Performance Parameters
Step 6: Predict Disease Class as
if ( class=1) Patient=Normal State
else_if(class =0) Patient=Disease State
end for
3.3 Dataset Description
Pima Indians Diabetes Database
This study used data sets from the Pima Indians Diabetes Database of National Institute of
Diabetes [14]. This dataset consists of 768 samples with 8 numerical valued attribute where 500
are tested negative and 268 are tested positive instances.
Chronic Kidney Disease Dataset
This study used data sets from the university of California Irvine (UCI) repository. The data set
contains 400 patients, where 250 patients were positively affected by kidney disease and as many
as 150 patients do not suffer from kidney disease.
Liver Disorders Data Set
This study used data sets from the university of California Irvine (UCI) repository. This data set
contains 416 liver patient records and 167 non liver patient records collected from North East of
Andhra Pradesh, India.
3.4 Performance Measures
In this study, we used three performance measures: Precision, Accuracy, Recall, F_measure and
Total execution time.
Accuracy is termed as ratio of the number of correctly classified instances to the total number of
instances.
Accuracy = (TP+TN)/(TP+TN+FP+FN)
Precision is the ration of actually true predicted instances out of the total true instances.
Precision = TP/(TP+FP)
Recall is the ratio of actual true instances out of all the items which are true.
Recall = TP/(TP+FN)
F-measure is the harmonic mean of both precision and recall.
F_Measure = 2*(Precision*Recall)/(Precision + Recall)
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Where TP, TN, FP and FN denote true positives, true negatives, false positives and false
negatives respectively.
4. RESULT ANALYSIS
Below Table 2 and 3 as well as Figure 3 shows the comparative analysis of proposed algorithm
with some existing algorithms.
Table 2: Result Analysis of Diabetes Disease Detection
Diabetes Disease Detection
Recall Precision Accuracy F_measure
1 0.9821 0.9935 0.991
Table 3: Comparative Result Analysis of Diabetes Disease Detection
Accuracy Measurement
Existing Work [14] 90.43%
Proposed Work 99.35%
Figure 3: Comparative Chart of Diabetes Disease Detection
Below Table 4, 5 and 6 shows the parameter values for different diseases such as diabetes disease,
kidney disease as well as liver disease. Similarly figure 4 and 5 shows the corresponding
parametric chart of different disease detection using proposed algorithm.
Table 4: Result Analysis of Kidney Disease Detection
Kidney Disease Detection
Recall Precision Accuracy F_measure
1 0.9875 0.9937 0.9937
Table 5: Result Analysis of Liver Disease Detection
Liver Disease Detection
Recall Precision Accuracy F_measure
0.9667 1 0.9914 0.9831
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Figure 4: Parameter Comparison Chart of Different Disease Detection
Figure 5: Time Comparison Chart of Different Disease Detection
5. CONCLUSION
This research paper is mainly focused to predict disease possibility using data mining or machine
learning approach in order to enhance the accuracy or precision of the disease detection expert
system. This paper also shows the related work study of different approaches such as neural
network, naïve bayes, SVM, KNN, FCN, etc and it is concluded that SVM gives the best
performance as compared to the other existing techniques. As a result of study the proposed
algorithm is designed using SVM and RF algorithm and the experimental result shows the
accuracy of 99.35%, 99.37 and 99.14 on diabetes, kidney and liver disease respectively. In future
using data mining approach a new optimized intelligent system can be designed which can give
accurate and efficient result.
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