This document proposes a constraint k-Means-Mode clustering algorithm to predict the likelihood of diseases using medical data containing both continuous and categorical attributes. It first maps complex medical data to mineable items using domain dictionaries and rule bases. The developed algorithm can handle both continuous and discrete data, perform clustering based on anticipated likelihood attributes with core disease attributes, and was tested on a real-world patient dataset to demonstrate its effectiveness.
IRJET- Heart Disease Prediction and RecommendationIRJET Journal
This document describes a study that developed a machine learning model to predict heart disease risk and provide recommendations. The study used a decision tree algorithm and the Cleveland heart disease dataset to train a model. The model takes in 14 clinical attributes to predict the risk of heart disease on a scale of 0 to 1. It then provides control measure recommendations based on the predicted risk level to help users reduce their risk. The system was designed to be implemented as an Android application for users to input their data and receive the prediction and recommendations.
Comparing Data Mining Techniques used for Heart Disease PredictionIRJET Journal
This document compares various data mining techniques for predicting heart disease, including neural networks, decision trees, and Naive Bayes classification. It analyzes past research applying these techniques to heart disease data and finds that neural networks achieved the highest accuracy of 100% when using 15 attributes. Decision tree techniques like C4.5, ID3, CART and J48 also performed well with accuracies over 90%. Naive Bayes classification achieved average accuracy of around 90%. The document concludes neural networks are the most effective technique for heart disease prediction when sufficient attributes are available.
IRJET- Prediction and Analysis of Heart Disease using SVM AlgorithmIRJET Journal
This document describes a study that uses a support vector machine (SVM) algorithm to predict heart disease based on patient data. The study uses a dataset of 1000 patient records with 8 attributes related to risk factors for heart disease. The SVM algorithm is applied to identify patterns in the data and classify patients as having heart disease or not. It aims to find the optimal decision boundary between the two classes to minimize classification errors. The results show that the SVM technique can accurately predict heart disease based on the risk factor attributes in the patient data.
Heart Attack Prediction System Using Fuzzy C Means ClassifierIOSR Journals
This document presents a heart attack prediction system using a fuzzy C-means classifier. The system utilizes 13 patient attributes as inputs to the fuzzy C-means classifier to determine the risk of a heart attack. The classifier was tested on medical records from 270 patients and achieved a classification accuracy of 92%. Fuzzy C-means clustering allows data points to belong to multiple clusters, providing a more efficient and cost-effective way to predict the likelihood of patients experiencing a heart attack compared to other algorithms.
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.
Human heart can be described as a compound body organ contains muscles together with
biological nerves. Human heart pumps nearly 5 litre of blood in the body providing the human body
with renewed material [6]. If operation of heart is not proper, it will affect the other body parts of
human such as brain, kidney etc. various study revealed that heart disease have emerged as the
number one killer in world. About 25 per cent of deaths in the age group of 25-69 years occur
because of heart disease. There are number of factors, which increase the risk of heart disease such
as smoking, cholesterol, high blood pressure, obesity and low physical exercise etc. The World
Health Organisation (WHO) has estimated that 12 million deaths occur worldwide, every year due to
heart diseases. WHO estimated by 2030, almost 23.6 million people will die due to Heart
disease.Cardiovascular disease includes coronary heart disease (CHD), cerebrovascular disease
(stroke), Hypertensive heart disease, congenital heart disease, peripheral artery disease, rheumatic
heart disease, inflammatory heart disease [5].
The document proposes a heart attack prediction system using fuzzy C-means clustering. The system takes in a patient's medical attributes like age, blood pressure, and artery thickness from their records. It then uses a fuzzy C-means algorithm to cluster this data and predict the patient's risk of a heart attack. The system is intended to help doctors make earlier diagnoses compared to only relying on their experience and a patient's records.
A major challenge facing healthcare organizations (hospitals, medical centers) is
the provision of quality services at affordable costs. Quality service implies diagnosing
patients correctly and administering treatments that are effective. Poor clinical decisions
can lead to disastrous consequences which are therefore unacceptable. Hospitals must
also minimize the cost of clinical tests. They can achieve these results by employing
appropriate computer-based information and/or decision support systems.
Most hospitals today employ some sort of hospital information systems to manage
their healthcare or patient data.
These systems are designed to support patient billing, inventory management and generation of simple statistics. Some hospitals use decision support systems, but they are largely limited. Clinical decisions are often made based on doctors’ intuition and experience rather than on the knowledge rich data hidden in the database.
This practice leads to unwanted biases, errors and excessive medical costs which affects the quality of service provided to patients.
IRJET- Heart Disease Prediction and RecommendationIRJET Journal
This document describes a study that developed a machine learning model to predict heart disease risk and provide recommendations. The study used a decision tree algorithm and the Cleveland heart disease dataset to train a model. The model takes in 14 clinical attributes to predict the risk of heart disease on a scale of 0 to 1. It then provides control measure recommendations based on the predicted risk level to help users reduce their risk. The system was designed to be implemented as an Android application for users to input their data and receive the prediction and recommendations.
Comparing Data Mining Techniques used for Heart Disease PredictionIRJET Journal
This document compares various data mining techniques for predicting heart disease, including neural networks, decision trees, and Naive Bayes classification. It analyzes past research applying these techniques to heart disease data and finds that neural networks achieved the highest accuracy of 100% when using 15 attributes. Decision tree techniques like C4.5, ID3, CART and J48 also performed well with accuracies over 90%. Naive Bayes classification achieved average accuracy of around 90%. The document concludes neural networks are the most effective technique for heart disease prediction when sufficient attributes are available.
IRJET- Prediction and Analysis of Heart Disease using SVM AlgorithmIRJET Journal
This document describes a study that uses a support vector machine (SVM) algorithm to predict heart disease based on patient data. The study uses a dataset of 1000 patient records with 8 attributes related to risk factors for heart disease. The SVM algorithm is applied to identify patterns in the data and classify patients as having heart disease or not. It aims to find the optimal decision boundary between the two classes to minimize classification errors. The results show that the SVM technique can accurately predict heart disease based on the risk factor attributes in the patient data.
Heart Attack Prediction System Using Fuzzy C Means ClassifierIOSR Journals
This document presents a heart attack prediction system using a fuzzy C-means classifier. The system utilizes 13 patient attributes as inputs to the fuzzy C-means classifier to determine the risk of a heart attack. The classifier was tested on medical records from 270 patients and achieved a classification accuracy of 92%. Fuzzy C-means clustering allows data points to belong to multiple clusters, providing a more efficient and cost-effective way to predict the likelihood of patients experiencing a heart attack compared to other algorithms.
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.
Human heart can be described as a compound body organ contains muscles together with
biological nerves. Human heart pumps nearly 5 litre of blood in the body providing the human body
with renewed material [6]. If operation of heart is not proper, it will affect the other body parts of
human such as brain, kidney etc. various study revealed that heart disease have emerged as the
number one killer in world. About 25 per cent of deaths in the age group of 25-69 years occur
because of heart disease. There are number of factors, which increase the risk of heart disease such
as smoking, cholesterol, high blood pressure, obesity and low physical exercise etc. The World
Health Organisation (WHO) has estimated that 12 million deaths occur worldwide, every year due to
heart diseases. WHO estimated by 2030, almost 23.6 million people will die due to Heart
disease.Cardiovascular disease includes coronary heart disease (CHD), cerebrovascular disease
(stroke), Hypertensive heart disease, congenital heart disease, peripheral artery disease, rheumatic
heart disease, inflammatory heart disease [5].
The document proposes a heart attack prediction system using fuzzy C-means clustering. The system takes in a patient's medical attributes like age, blood pressure, and artery thickness from their records. It then uses a fuzzy C-means algorithm to cluster this data and predict the patient's risk of a heart attack. The system is intended to help doctors make earlier diagnoses compared to only relying on their experience and a patient's records.
A major challenge facing healthcare organizations (hospitals, medical centers) is
the provision of quality services at affordable costs. Quality service implies diagnosing
patients correctly and administering treatments that are effective. Poor clinical decisions
can lead to disastrous consequences which are therefore unacceptable. Hospitals must
also minimize the cost of clinical tests. They can achieve these results by employing
appropriate computer-based information and/or decision support systems.
Most hospitals today employ some sort of hospital information systems to manage
their healthcare or patient data.
These systems are designed to support patient billing, inventory management and generation of simple statistics. Some hospitals use decision support systems, but they are largely limited. Clinical decisions are often made based on doctors’ intuition and experience rather than on the knowledge rich data hidden in the database.
This practice leads to unwanted biases, errors and excessive medical costs which affects the quality of service provided to patients.
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.
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
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)
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD Editor
This document summarizes a research paper that proposes using fuzzy logic and data mining techniques to predict medical diseases from patient data. The paper extracts meaningful patterns of diseases from clinical guidelines using text mining. It then applies fuzzy logic to patient data through the clinical guidelines to determine the possibility of different diseases. Prior work on applying data mining to medical data is also reviewed, highlighting challenges like a lack of standardized clinical vocabulary and issues with data cleaning. The proposed approach uses fuzzy rules sets and association mining on clinical guidelines to predict medical diseases.
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
Survey on data mining techniques in heart disease predictionSivagowry Shathesh
This document summarizes research on using data mining techniques to predict heart disease. It discusses previous work using classification, clustering, association rule mining and other techniques on several heart disease datasets. Classification algorithms like naive bayes, decision trees and neural networks have been widely used with naive bayes found to often provide the best performance. Feature selection and attribute reduction are also examined. The document provides an overview of the key steps and techniques in medical data mining and predictive analysis for heart disease.
Heart Disease Identification Method Using Machine Learnin in E-healthcare.SUJIT SHIBAPRASAD MAITY
This document describes a student project that aims to develop a machine learning model for heart disease identification and prediction. It discusses existing heart disease diagnosis techniques, identifies the problem and requirements, outlines the proposed algorithm and methodology using supervised learning classification algorithms like K-Nearest Neighbors and logistic regression. Block diagrams and flow charts illustrate the data preprocessing, model training, and web application development steps to classify patients as having heart disease or not and evaluate model performance. The developed system achieves high accuracy for heart disease prediction.
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.
Disease Prediction And Doctor Appointment systemKOYELMAJUMDAR1
This document outlines a disease prediction and doctor appointment system using machine learning. The objectives are to provide quick medical diagnosis to rural patients and enhance access to medical specialists. Five machine learning algorithms - Decision Tree, Random Forest, Naive Bayes, K-Nearest Neighbors, and Support Vector Machine - are used for disease prediction. The system displays predicted diseases and accuracy scores for each algorithm. Users can then book appointments with specialist doctors for their predicted disease.
A Heart Disease Prediction Model using Decision TreeIOSR Journals
This document presents a heart disease prediction model using decision tree analysis. It selects 14 clinical features from patient data and develops prediction models using the J48 decision tree algorithm with unpruned, pruned, and pruned with reduced error pruning approaches. The results show that the pruned J48 decision tree with reduced error pruning has the highest accuracy at 75.73%, compared to 72.82% for unpruned and 73.79% for pruned. The reduced error pruning approach produces more compact decision rules with fewer extracted rules, improving predictive performance.
Smart health disease prediction python djangoShaikSalman28
mca final year project of Smart health disease prediction python django ppt. It is also helpful for mtech students also. Can anyone need this project coding then call me 9491831577. if you want extra projects then also u can call me . Smart health disease prediction python django price is 300rs.
Survey on data mining techniques in heart disease predictionSivagowry Shathesh
This document describes a study on applying data mining techniques to analyze and predict heart disease. It discusses how data mining can extract valuable knowledge from healthcare data. The study uses several data mining techniques like decision trees, naive Bayes classification, clustering, and association rule mining on heart disease datasets from UC Irvine to predict heart disease. Experimental results show that multilayer neural networks and classification techniques like naive Bayes had higher prediction accuracy compared to other methods.
HEART DISEASE PREDICTION USING NAIVE BAYES ALGORITHMamiteshg
This document describes using a Naive Bayes classifier to predict the likelihood of heart disease. It discusses how a web-based application would take in a user's medical information and use a trained dataset to compare and retrieve hidden data to diagnose heart disease. The document provides an example of using Bayes' theorem to calculate the probability of breast cancer based on a positive mammogram. It explains the implementation of the Naive Bayes classifier and concludes that the model could help practitioners make accurate clinical decisions to diagnose and treat heart disease.
This document presents a comparative study of various data mining techniques for predicting heart disease using collected and standard heart disease datasets. Five classifiers - KStar, J48, SMO, Bayes Net, and MLP - were evaluated based on their accuracy and training time. SMO had the highest accuracy of 84-89% and MLP had the lowest training time of 0.33-0.75 seconds. The techniques are also compared based on their average classification variance. The study concludes with receiver operating characteristic curves showing the performance of the techniques on the two datasets.
Hybrid Technique for Associative Classification of Heart DiseasesJagdeep Singh Malhi
The document discusses a hybrid technique for associative classification of heart diseases using data mining. It summarizes existing classification and association rule mining algorithms applied to heart disease data. The author aims to improve accuracy by generating classification association rules efficiently and integrating classification with association rule mining. The proposed approach is implemented in Weka to extract rules from a heart disease dataset using Apriori and FP-Growth algorithms. The rules are used to classify patients and evaluate the performance compared to other methods.
IRJET- Disease Prediction using Machine LearningIRJET Journal
This document discusses using machine learning techniques to predict diseases based on patient symptoms. Specifically, it proposes using naive bayes, k-nearest neighbors (KNN), and logistic regression algorithms on structured and unstructured hospital data to predict diseases like diabetes, malaria, jaundice, dengue, and tuberculosis. The system is intended to make disease prediction more accessible to end users by analyzing their symptoms without needing to visit a doctor. It aims to improve prediction accuracy by handling both structured and unstructured data using machine learning models.
A data mining approach for prediction of heart disease using neural networksIAEME Publication
This document describes a study that developed a heart disease prediction system using neural networks and data mining techniques. [1] It used a multilayer perceptron neural network with backpropagation algorithm to predict heart disease based on 13 medical parameters from patient data. [2] To improve accuracy, it added two more parameters - obesity and smoking. [3] The system was able to predict heart disease with nearly 100% accuracy based on testing 270 patient records.
Data mining techniques on heart failure diagnosisSteve Iduye
The document discusses using data mining techniques to diagnose coronary artery disease (CAD) through three case studies. Case 1 uses association rule mining on the Cleveland dataset to identify risk factors for CAD. Case 2 uses decision trees and bagging algorithms on laboratory and echocardiography features to diagnose CAD. Case 3 applies classification algorithms like SMO and Naive Bayes as well as feature selection and creation to the Z-Alizadeh Sani dataset to predict artery stenosis. The studies demonstrate how data mining can effectively analyze medical data and extract rules to diagnose CAD.
This document discusses using biologically inspired machine learning techniques to categorize tumor types. It proposes applying genetic search, particle swarm optimization, and evolutionary search algorithms to a dataset with 18 attributes and 339 tumor instances to eliminate irrelevant features before classification. The results are evaluated using performance metrics and show biologically inspired models like multi-layer perceptron with optimization techniques can help medical experts efficiently predict and diagnose tumors.
Finding Symmetric Association Rules to Support Medical Qualitative Researchrazanpaul
This document proposes an algorithm to discover both asymmetric and symmetric relationships between medical attributes from patient data. Existing algorithms only find asymmetric relationships or relationships between frequent items. The proposed algorithm allows medical researchers to specify constraints like minimum support and confidence for groups of attributes, which attributes can appear in the antecedent, consequent, or both. It maps complex medical data like numbers and text to items. It generates candidate itemsets based on group constraints and uses support to find desired itemsets. The goal is to find meaningful symmetric relationships between specified medical attributes.
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
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.
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
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)
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD Editor
This document summarizes a research paper that proposes using fuzzy logic and data mining techniques to predict medical diseases from patient data. The paper extracts meaningful patterns of diseases from clinical guidelines using text mining. It then applies fuzzy logic to patient data through the clinical guidelines to determine the possibility of different diseases. Prior work on applying data mining to medical data is also reviewed, highlighting challenges like a lack of standardized clinical vocabulary and issues with data cleaning. The proposed approach uses fuzzy rules sets and association mining on clinical guidelines to predict medical diseases.
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
Survey on data mining techniques in heart disease predictionSivagowry Shathesh
This document summarizes research on using data mining techniques to predict heart disease. It discusses previous work using classification, clustering, association rule mining and other techniques on several heart disease datasets. Classification algorithms like naive bayes, decision trees and neural networks have been widely used with naive bayes found to often provide the best performance. Feature selection and attribute reduction are also examined. The document provides an overview of the key steps and techniques in medical data mining and predictive analysis for heart disease.
Heart Disease Identification Method Using Machine Learnin in E-healthcare.SUJIT SHIBAPRASAD MAITY
This document describes a student project that aims to develop a machine learning model for heart disease identification and prediction. It discusses existing heart disease diagnosis techniques, identifies the problem and requirements, outlines the proposed algorithm and methodology using supervised learning classification algorithms like K-Nearest Neighbors and logistic regression. Block diagrams and flow charts illustrate the data preprocessing, model training, and web application development steps to classify patients as having heart disease or not and evaluate model performance. The developed system achieves high accuracy for heart disease prediction.
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.
Disease Prediction And Doctor Appointment systemKOYELMAJUMDAR1
This document outlines a disease prediction and doctor appointment system using machine learning. The objectives are to provide quick medical diagnosis to rural patients and enhance access to medical specialists. Five machine learning algorithms - Decision Tree, Random Forest, Naive Bayes, K-Nearest Neighbors, and Support Vector Machine - are used for disease prediction. The system displays predicted diseases and accuracy scores for each algorithm. Users can then book appointments with specialist doctors for their predicted disease.
A Heart Disease Prediction Model using Decision TreeIOSR Journals
This document presents a heart disease prediction model using decision tree analysis. It selects 14 clinical features from patient data and develops prediction models using the J48 decision tree algorithm with unpruned, pruned, and pruned with reduced error pruning approaches. The results show that the pruned J48 decision tree with reduced error pruning has the highest accuracy at 75.73%, compared to 72.82% for unpruned and 73.79% for pruned. The reduced error pruning approach produces more compact decision rules with fewer extracted rules, improving predictive performance.
Smart health disease prediction python djangoShaikSalman28
mca final year project of Smart health disease prediction python django ppt. It is also helpful for mtech students also. Can anyone need this project coding then call me 9491831577. if you want extra projects then also u can call me . Smart health disease prediction python django price is 300rs.
Survey on data mining techniques in heart disease predictionSivagowry Shathesh
This document describes a study on applying data mining techniques to analyze and predict heart disease. It discusses how data mining can extract valuable knowledge from healthcare data. The study uses several data mining techniques like decision trees, naive Bayes classification, clustering, and association rule mining on heart disease datasets from UC Irvine to predict heart disease. Experimental results show that multilayer neural networks and classification techniques like naive Bayes had higher prediction accuracy compared to other methods.
HEART DISEASE PREDICTION USING NAIVE BAYES ALGORITHMamiteshg
This document describes using a Naive Bayes classifier to predict the likelihood of heart disease. It discusses how a web-based application would take in a user's medical information and use a trained dataset to compare and retrieve hidden data to diagnose heart disease. The document provides an example of using Bayes' theorem to calculate the probability of breast cancer based on a positive mammogram. It explains the implementation of the Naive Bayes classifier and concludes that the model could help practitioners make accurate clinical decisions to diagnose and treat heart disease.
This document presents a comparative study of various data mining techniques for predicting heart disease using collected and standard heart disease datasets. Five classifiers - KStar, J48, SMO, Bayes Net, and MLP - were evaluated based on their accuracy and training time. SMO had the highest accuracy of 84-89% and MLP had the lowest training time of 0.33-0.75 seconds. The techniques are also compared based on their average classification variance. The study concludes with receiver operating characteristic curves showing the performance of the techniques on the two datasets.
Hybrid Technique for Associative Classification of Heart DiseasesJagdeep Singh Malhi
The document discusses a hybrid technique for associative classification of heart diseases using data mining. It summarizes existing classification and association rule mining algorithms applied to heart disease data. The author aims to improve accuracy by generating classification association rules efficiently and integrating classification with association rule mining. The proposed approach is implemented in Weka to extract rules from a heart disease dataset using Apriori and FP-Growth algorithms. The rules are used to classify patients and evaluate the performance compared to other methods.
IRJET- Disease Prediction using Machine LearningIRJET Journal
This document discusses using machine learning techniques to predict diseases based on patient symptoms. Specifically, it proposes using naive bayes, k-nearest neighbors (KNN), and logistic regression algorithms on structured and unstructured hospital data to predict diseases like diabetes, malaria, jaundice, dengue, and tuberculosis. The system is intended to make disease prediction more accessible to end users by analyzing their symptoms without needing to visit a doctor. It aims to improve prediction accuracy by handling both structured and unstructured data using machine learning models.
A data mining approach for prediction of heart disease using neural networksIAEME Publication
This document describes a study that developed a heart disease prediction system using neural networks and data mining techniques. [1] It used a multilayer perceptron neural network with backpropagation algorithm to predict heart disease based on 13 medical parameters from patient data. [2] To improve accuracy, it added two more parameters - obesity and smoking. [3] The system was able to predict heart disease with nearly 100% accuracy based on testing 270 patient records.
Data mining techniques on heart failure diagnosisSteve Iduye
The document discusses using data mining techniques to diagnose coronary artery disease (CAD) through three case studies. Case 1 uses association rule mining on the Cleveland dataset to identify risk factors for CAD. Case 2 uses decision trees and bagging algorithms on laboratory and echocardiography features to diagnose CAD. Case 3 applies classification algorithms like SMO and Naive Bayes as well as feature selection and creation to the Z-Alizadeh Sani dataset to predict artery stenosis. The studies demonstrate how data mining can effectively analyze medical data and extract rules to diagnose CAD.
This document discusses using biologically inspired machine learning techniques to categorize tumor types. It proposes applying genetic search, particle swarm optimization, and evolutionary search algorithms to a dataset with 18 attributes and 339 tumor instances to eliminate irrelevant features before classification. The results are evaluated using performance metrics and show biologically inspired models like multi-layer perceptron with optimization techniques can help medical experts efficiently predict and diagnose tumors.
Finding Symmetric Association Rules to Support Medical Qualitative Researchrazanpaul
This document proposes an algorithm to discover both asymmetric and symmetric relationships between medical attributes from patient data. Existing algorithms only find asymmetric relationships or relationships between frequent items. The proposed algorithm allows medical researchers to specify constraints like minimum support and confidence for groups of attributes, which attributes can appear in the antecedent, consequent, or both. It maps complex medical data like numbers and text to items. It generates candidate itemsets based on group constraints and uses support to find desired itemsets. The goal is to find meaningful symmetric relationships between specified medical attributes.
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
Prediction of heart disease using classification mining technique on sparkdbpublications
This paper identifies the increasing health care data which is being accumulated digitally every day. The healthcare industry is becoming very data intensive. Worldwide digital healthcare data is estimated to be equal to 500 petabytes (1015 bytes), and is expected to reach 25 exabytes (1018 bytes) in 2020 [6].In this paper, heart disease is one such disease selected among variety of disease in healthcare. The purpose of this work is to predict the diagnosis of heart disease with reduced number of attributes. Each dataset stored in HDFS is classified based on attributes. This prediction solution using random forest on apache spark gives massive opportunity for health care analysts to deploy this solution on ever changing, scalable big data landscape for insightful decision making.
CLUSTERING DICHOTOMOUS DATA FOR HEALTH CAREijistjournal
Dichotomous data is a type of categorical data, which is binary with categories zero and one. Health care data is one of the heavily used categorical data. Binary data are the simplest form of data used for heath care databases in which close ended questions can be used; it is very efficient based on computational efficiency and memory capacity to represent categorical type data. Clustering health care or medical data is very tedious due to its complex data representation models, high dimensionality and data sparsity. In this paper, clustering is performed after transforming the dichotomous data into real by wiener transformation. The proposed algorithm can be usable for determining the correlation of the health disorders and symptoms observed in large medical and health binary databases. Computational results show that the clustering based on Wiener transformation is very efficient in terms of objectivity and subjectivity.
CLUSTERING DICHOTOMOUS DATA FOR HEALTH CAREijistjournal
This document discusses clustering dichotomous health care data using the K-means algorithm after transforming the data using Wiener transformation. It begins with an introduction to dichotomous data and the challenges of clustering medical data. It then describes the K-means clustering algorithm and various distance measures used for binary data clustering. The document proposes using Wiener transformation to first transform binary data to real values before applying K-means clustering. It evaluates the results on a lens dataset using inter-cluster and intra-cluster distances, finding the transformed data yields better clusters than the original binary data according to these metrics.
Android Based Questionnaires Application for Heart Disease Prediction Systemijtsrd
Today classification techniques in data mining are most popular to prediction and data exploration. This Heart Disease Prediction System HDPS is using Naive Bayesian Classification with a comparison for simple probability and that of Jelinek Mercer JM Smoothing. It is implemented as an Android based application user must be feedback and answers the questions then can be seen the result as user desired in different ways exactly heart disease is present or not and then with predictions No, Low, Average, High, Very High . And the system will be provided required suggestions such as doctor details and medications to patients could be able. It will be also proved that enhanced Naive Bayes with Jelinek Mercer smoothing technique is also effective to eliminate the noise for prediction the heart disease. This system can also calculate classifier accuracy by using precision and recall. Nan Yu Hlaing | Phyu Pyar Moe "Android Based Questionnaires Application for Heart Disease Prediction System" 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/ijtsrd26750.pdfPaper URL: https://www.ijtsrd.com/computer-science/data-miining/26750/android-based-questionnaires-application-for-heart-disease-prediction-system/nan-yu-hlaing
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
The document summarizes a disease prediction system for rural health services presented by two students. The key points are:
1. The system aims to provide quick medical diagnosis to rural patients using machine learning algorithms like SVM, RF, DT, NB, ANN, KNN, and LR to recognize diseases from symptoms.
2. It seeks to enhance access to medical specialists for rural communities and improve quality of healthcare.
3. The expected outcomes are conducting experiments to evaluate the performance of using 7 machine learning algorithms to predict diseases from symptoms and having doctors select the correct diagnosis from the predictions.
This document provides an overview of Bayesian statistics and its applications in data mining for pharmacovigilance. It discusses how Bayesian statistics uses probability to represent subjective degrees of belief and incorporates prior knowledge. It also describes how the Bayesian Confidence Propagation Neural Network (BCPNN) is used as a hypothesis generating tool to identify unexpected drug-adverse event associations in large safety databases. The BCPNN calculates an Information Component value to indicate the strength of associations. Bayesian approaches are advantageous for pharmacovigilance as they allow incorporation of external evidence and handle missing data.
An overview of the i2b2 clinical research platform, and the implications of connecting Indivo to i2b2 as a source of patient-reported outcomes. Presented at the 2012 Indivo X Users' Conference.
By Shawn Murphy MD, Ph.D., Partners Healthcare.
IRJET- Prediction of Heart Disease using RNN AlgorithmIRJET Journal
This document discusses using a recurrent neural network (RNN) algorithm to predict heart disease. It proposes a method called prognosis prediction using RNN (PP-RNN) that uses multiple RNNs to learn from patient diagnosis code sequences in order to predict high-risk diseases. The experimental results show that the proposed PP-RNN method can achieve more accurate results than existing methods for predicting heart disease risk. It also provides background on related works using other techniques like decision trees, clustering, and AdaBoost for heart disease prediction.
Analysis on Data Mining Techniques for Heart Disease DatasetIRJET Journal
This document analyzes various data mining techniques for classifying heart disease datasets. It compares the performance of classification algorithms like decision trees and lazy learning on aspects like time taken to build models. The algorithms are tested on a heart disease dataset from a public repository using the KEEL data mining tool. Decision trees and k-nearest neighbors are implemented using distance functions like Euclidean and HVDM across different validation modes. The results show that k-nearest neighbors with no validation is the most efficient algorithm for predicting heart disease, taking the least time to build models of the dataset. The study aims to determine the optimal classification algorithm for heart disease prediction systems.
The document describes a proposed clinical decision support system that uses k-means clustering and an artificial neural network with particle swarm optimization to classify patient data and determine diagnoses. It begins with background on clinical decision making and existing systems. It then outlines the proposed system, which involves clustering patient data using k-means, and training an artificial neural network using particle swarm optimization and backpropagation to classify new patient data and determine optimal treatment. The combination of these techniques is meant to improve accuracy, efficiency, time consumption and costs compared to other methods.
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.
AN ALGORITHM FOR PREDICTIVE DATA MINING APPROACH IN MEDICAL DIAGNOSISijcsit
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.
Intelligent data analysis for medicinal diagnosisIRJET Journal
The document describes a proposed privacy-preserving patient-centric clinical decision support system called PPCD that uses naive Bayesian classification to help doctors predict disease risks for patients in a privacy-preserving manner. PPCD allows medical diagnosis and prediction of disease risks for new patients without leaking any individual patient medical information. It utilizes historical medical information from past patients, stored privately in the cloud, to train a naive Bayesian classifier. This trained classifier can then be used to diagnose diseases for new patients based on their symptoms while preserving privacy. The system also introduces a new aggregation technique called additive homomorphic proxy aggregation to allow training of the naive Bayesian classifier without revealing individual patient medical records.
IRJET- Disease Prediction using Machine LearningIRJET Journal
This document discusses using machine learning algorithms to predict diseases based on patient health data. Specifically, it proposes using a k-means machine learning algorithm to analyze structured and unstructured patient data stored in a healthcare dataset. This would allow the system to predict diseases and outbreaks with greater accuracy than existing methods. The k-means algorithm is applied to cluster patient data, including symptoms from sensors and medical records, to identify patterns and deliver predictive results. The goal is to enable early disease prediction and prevention through analysis of big healthcare data using machine learning.
Prediction of Neurological Disorder using Classification ApproachBRNSSPublicationHubI
This document summarizes a research article about predicting neurological disorders using classification approaches. The researchers tested various classification algorithms on brain morphology data to distinguish between healthy individuals and patients with neurological disorders. They found that the KNN-MinMax classifier was the most effective, achieving prediction accuracy rates 6-8% higher than existing methods. The proposed methodology involved feature extraction, selection, and classification of the data using algorithms like KNN, PCA, random forest, and neural networks.
Early Identification of Diseases Based on Responsible Attribute using Data Mi...IRJET Journal
This document describes a proposed method for early identification of diseases using data mining and classification techniques. It begins with an introduction to classification and discusses how it is commonly used in healthcare for tasks like predicting patient risk levels. It then reviews related literature applying classification methods to diseases like heart disease and diabetes. The document outlines the problem of selecting the best classification technique for a given healthcare dataset. It proposes an architecture and method for disease prediction that assigns recommended values to attributes and classifies unknown data based on calculating totals. The method is experimentally analyzed using a heart disease dataset, and its accuracy is compared to Bayesian classification. In conclusion, the proposed method seeks to reduce attributes and complexity while accurately classifying patient data for early disease identification.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Similar to Clustering Medical Data to Predict the Likelihood of Diseases (20)
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HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
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Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
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These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
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2. numerical data for each attribute to a series of items. cardinality of attributes except continuous numeric
For example, there are certain conventions to data are not high in medical domain, these attribute
consider a person is young, adult, or elder with values are mapped to integer values using medical
respect to age. A set of rules is created for each domain dictionaries. Therefore, the mapping process
continuous numerical attribute using the knowledge is divided in two phases. Phase 1: a rule base is
of medical domain experts. A rule engine is used to constructed based on the knowledge of medical
map continuous numerical data to items using these domain experts and dictionaries are constructed for
developed rules. attributes where domain expert knowledge is not
applicable, Phase 2: attribute values are mapped to
We have used domain dictionary approach to integer values using the corresponding rule base and
transform the data, for which medical domain expert the dictionaries.
knowledge is not applicable, to numerical form. As
Original Mapped Original Mapped
Generate dictionary for value value value value
each categorical attribute Headache 1 Yes 1
Fever 2 No 2
PatientActual Data
Age Smoke Diagnosis Dictionary of Dictionary of
ID Diagnosis attribute Smoke attribute
1020D 33 Yes Headache
1021D 63 No Fever Map to integer items using
rule base and dictionaries
Actual data
If age <= 12 then 1
Medical If 13<=age<=60 then 2
domain If 60 <=age then 3 Patient Age Smoke Diagnosis
knowledge If smoke = y then 1 ID
If smoke = n then 2 1020D 2 1 1
If Sex = M then 1
1021D 3 2 2
If Sex = F then 2
Rule Base Data suitable for Knowledge Discovery
Figure 1. Data transformation of medical data
3.1. Updating cluster center
We need to update the k clusters centre
3. The proposed algorithm dynamically in order to minimize the intra cluster
distance of patients. Here k is the number of clusters
Figure 2 shows the proposed hybrid-partitioning
we would like to make and Pi is the ith patient
algorithm, which can handle both continuous and
attribute and Ci is the ith mean-mode value of cluster
discrete data and perform clustering based on
C. As the patient attributes are both continuous and
anticipated likelihood attributes with core attributes
discrete, each cluster center is an array of both
of disease in data point. In this algorithm, the user
average and mode values where average and mode
will set which attributes will be used as data point for
are computed for continuous and discrete attributes
a patient and which attributes will participate in
respectively. Mean is computed for each continuous
clustering process. The goal of this algorithm is
attribute by calculating average of that attribute
making clusters to find likelihood. Healthcare data
among the data points in that cluster. Mode is
are sparse as doctors perform only few different
computed for each discrete attribute by calculating
clinical lab tests for a patient over his lifetime. This
maximum frequent value of that attribute among the
is natural many patients have not all anticipated
data points in that cluster.
attributes for likelihood. When a patient does not
have one or more anticipated attributes for
likelihood, keeping this patient in clustering process 3.2. Dissimilarity measure
will make clusters useless to find likelihood.
Therefore, we are ignoring that patient in the The object dissimilarity measure is derived from
clustering process. both numeric and categorical attributes. For discrete
features, the dissimilarity measure between two data
point depends on the number of different values in
45
3. Algorithm: Partition patients to find likelihood of 1.2.1 If A is continuous attribute
disease based on MeanMode value of patients. 1.2.1.1 MeanModec [i] = Find the mean
1. Read the metadata about which attributes will only among the attribute named A values of data points
appear in clustering process. in cluster c.
2. Partition patient data into k cluster in random and assign 1.2.2 else If A is category attribute
each partition to each cluster. To retrieve paient data use 1.2.2.1 MeanModec [i] = Find the mode
the corresponding RetrieveAllPatientsRecord() for each among the attribute named A values of data points
data model. in cluster c.
3. Repeat 1.2.3 i++;
3.1 Call UpdateMeanModeofClusters(K, M ) to
update Mean-Mode value of k clusters Procedure Distance (P: Patient, C: Cluster, m: Number
3.2 Move patient Pi to the cluster with least of attributes)
distance and find the distance between a patient //Here Pi represent the ith attribute value of Patient P and Ci
and a cluster using the function Distance (P, C, represents ith MeanMode value of Cluster C
m);
Until no patient is moved 1. for i = 1 to m where ith attribute value of Patient
can appear in clustering
Procedure UpdateMeanModeofClusters(K: Number of 1.1 If Pi is continuous
clusters, M: Medical attributes) 1.1.1 Then D1 = D1+ (Pi - Ci) 2
1. For each cluster c K 1.2 Else (categorical)
1.2.1 Then D2 = D2 + NumberofOnes (Pi ^ Ci);
1.1 i = 0 1.3 d = SQRT (D1) + D2;
1.2 For each attribute A M where A can appear in 2. return d;
clustering
Figure 2. Constraint k-Means-Mode clustering algorithm
each categorical feature. For continuous features, the Distance between based on continuous
dissimilarity measure between two data point
depends on Euclidean distance. Here we have used attributes is =1 ( )2 where , and n
the following two functions to measure dissimilarity: is the number of patients. Distance is measured using
hamming distance function for categorical Hamming distance function for categorical attributes.
objects and Euclidean distance function for Distance between based on categorical
continuous data. To measure distance between two
attributes is = ( , ) where , =
objects based on several features, for each feature we
0 ==
test whether this feature is discrete or continuous. If
the feature is continuous, distance is measured using 1
Euclidean distance and added it to D1 and if the
feature is discrete, the dissimilarity is measured 3.3. Likelihood
using hamming distance and added it to D2. The
resultant distance is computed by adding square root Likelihood is the probability of a specified
of D1 with D2. The computational complexity of the outcome. After clustering using constrained K-
algorithm is O ((I+1) k p), where p is the number of Means-Mode algorithm we get a set of clusters,
patients, k the number of clusters and I is the number C = {c1 , c2 , c3 , ck }. Each cluster contains a set
of iterations. of data points, which consist of anticipated
Let the anticipated likelihood attributes be = likelihood attributes and core attributes of disease.
{ 1 , 2 , 3 , . . . }. Let the core attributes of disease, Data points for cluster cj is
= { 1, 2, 3, }. In the clustering Dj = {dj1 , dj2 , dj3 , . . dju }. There are a set of
process, only anticipated likelihood attributes boolean functions on core attributes of disease to
participate. The anticipated likelihood attributes determine whether a data point has the presence of a
consist of both continuous and categorical attribute. disease or not. Let the set of boolean functions be
Let first attributes of are continuous and the F = {f1 , f2 , f3 , fv }. A data point dt has presence
remaining attributes are categorical. Let the of the disease if v fi (dt ) == true for the data
i=1
anticipated likelihood attributes of two data points point. In a cluster, the number of data points which
are . Dissimilarity between the anticipated has presence of the disease is u v
j=1 i=1 fi (dj ). The
likelihood attributes of two data points is the sum of number of total data points in the cluster is u u. j=1
dissimilarity of continuous attribute and dissimilarity So likelihood of a cluster for the disease is
of categorical attribute. Distance is measured using
Euclidian distance function for continuous attributes.
46
4. u v
j=1 i=1 f i d j Microsoft Vista and implementation language was
u u where fi is the function, which returns
j=1 c#. We used 2 datasets to verify our method. The
either one or zero. first data set of interest is patient dataset collected
Here each cluster is represented by the mean and preprocessed from Bangladeshi hospitals, which
mode value of that cluster. Now we will find the has 50273 instances with 514 attributes (included
equation of mean mode value of a cluster c. Mean is 150 discrete and 364 numerical attributes). The
calculated among the continuous attributes and mode Patient Dataset was clustered in 5 classes (Very High
is calculated among the categorical attributes. Let the Risk, High Risk, Medium Risk, Low Risk, No Risk)
mean mode value of a cluster be MM = using proposed algorithm to find likelihood of
mm1 , mm2 , mm3 , mmz where z is the number Diabetic. The next data set of interest is the Zoo Data
of attributes in the clustering process. Let first y Set [15] from UCI Machine Learning Repository,
attributes of MM are continuous and remaining which has the similar characteristics like medical
z y are categorical. The continuous part of mean data. It contains 101 instances with 7 classes
mode value is MMi i=1, y = the mean among ith {mammal, bird, reptile, fish, amphibian, insect, and
attribute values of cluster c. The categorical part of invertebrate}, each described by 18 attributes
(included 16 discrete and 2 numerical attributes). We
mean mode value is MMj = the mode
j=y+1, z have taken an average value from 10 trials for each
among jth attribute values of cluster c. of the test result. Likelihood is the probability of a
specified disease. Here average likelihood is the
4. Results and discussion average of all cluster likelihood. Actual likelihood is
the actual probability of the disease in the data,
The experiments were done using PC with core 2 which has been found using brute force approach.
duo processor with a clock rate of 1.8 GHz and 3GB Accuracy is the ratio between average likelihood and
of main memory. The operating system was actual likelihood.
K-Means K-Mode
K-Means with BK K-Mode with BK
K-Means-Mode K-Means-Mode with BK
1
Accuracy
0.5
0
64 47 33
Number of boolean functions
Figure 3. Accuracy of test result for the patient dataset to find likelihood of diabetic
For the Patient Dataset to find likelihood of without background knowledge achieves an average
Diabetic, Figure 3 presents accuracy results for K- accuracy of 17.7%. Both K-mode without
Means, K-Mode, K-Means-Mode, K-Means with background knowledge and K-mode with
background knowledge (BK), K-Mode with BK and background knowledge perform much worse,
K-Means-Mode with BK algorithms over the number averaging 12.1% and 30.2 accuracy respectively. The
of boolean functions. The number of boolean proposed method gives better results about 39-40%
functions for each presented result is also indicated. over k-means with background knowledge as
It shows that an average accuracy of 95.1% is illustrated in Figures 1 and about 64-65% over k-
achieved using the medical background information mode with background knowledge as illustrated in
and hybrid clustering algorithm. K-means algorithm Figures 1. The proposed method also gives much
with background knowledge (BK) achieves an better accuracy when compared to the k-means and
average accuracy of 56%. K-means algorithm K-Mode with about 77-78% over k-means and about
47
5. 82-83% over k-mode. It shows that an average 28%. What this demonstrates is that neither the
accuracy of 30.2-56% can be achieved by K-Means medical background information nor hybrid-
or K-Mode using the background information alone. clustering algorithm alone performs very well, but
K-Means-Mode algorithm without background combining the two effectively produces excellent
knowledge achieves an average accuracy of about results.
K-Means K-Mode
K-Means with BK K-Mode with BK
K-Means-Mode K-Means-Mode with BK
1
Accuracy
0.5
0
87 63 12
Number of boolean functions
Figure 4. Accuracy of test result for the zoo data set
For the Zoo Data Set [15], Figure 4 shows combining the two effectively produces excellent
accuracy results for K-Means, K-Mode K-Means- results.
Mode, K-Means with background knowledge (BK),
K-Mode with BK and K-Means-Mode with BK 6. References
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