NDLI: MV5: A Clinical Decision Support Framework for Heart Disease Prediction Using Majority Vote Based Classifier Ensemble

Content Provider	Springer Nature Link
Author	Bashir, Saba Qamar, Usman Khan, Farhan Hassan Javed, M. Younus
Copyright Year	2014
Abstract	The medical diagnosis process can be interpreted as a decision-making process during which the physician induces the diagnosis of a new and unknown case from an available set of clinical data and using his/her clinical experience. This process can be computerized in order to present medical diagnostic procedures in a rational, objective, accurate and efficient way. In the last few decades, many researchers have focused on developing effective methods for intelligent heart disease prediction and decision support systems. For such a system, high accuracy of prediction is paramount. In this research, an ensemble classifier is proposed, which uses majority vote-based scheme for heart disease data classification and prediction. The five heterogeneous classifiers used to construct the ensemble model are as follows: Naïve Bayes, decision tree based on Gini Index, decision tree based on information gain, memory-based learner and support vector machine. Five datasets from different data repositories are employed for testing the effectiveness of the ensemble model. Each dataset has different types of attributes, for instance binary, real, continuous, categorical, etc. Experimental results with stratified cross validation show that the proposed MV5 framework deals with all the attribute types. MV5 has achieved an accuracy of 88.52% with 86.96% sensitivity, 90.83% specificity and 88.85% f-measure. Comparison of proposed MV5 model with individual classifiers shows increase in average accuracy, sensitivity, specificity and f-measure of about 14, 11, 17 and 18% respectively.
Starting Page	7771
Ending Page	7783
Page Count	13
File Format	PDF
ISSN	13198025
Journal	Arabian Journal for Science and Engineering
Volume Number	39
Issue Number	11
e-ISSN	21914281
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2014-08-27
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Ensemble Majority vote Cross validation Heterogeneous classifiers Naïve Bayes Decision tree Gini Index Information gain Memory-based learner Support vector machine Engineering Science
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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