Use of Efficient Machine Learning Techniques in the Identification of Patients with Heart Diseases

Pronab Ghosh, Sami Azam, Asif Karim, Mirjam Jonkman, MD. Zahid Hasan

Research output: Chapter in Book/Report/Conference proceedingConference Paper published in Proceedingspeer-review

Abstract

Cardiovascular disease has become one of the world's major causes of death. Accurate and timely diagnosis is of crucial importance. We constructed an intelligent diagnostic framework for prediction of heart disease, using the Cleveland Heart disease dataset. We have used three machine learning approaches, Decision Tree (DT), K-Nearest Neighbor (KNN), and Random Forest (RF) in combination with different sets of features. We have applied the three techniques to the full set of features, to a set of ten features selected by "Pearson's Correlation"technique and to a set of six features selected by the Relief algorithm. Results were evaluated based on accuracy, precision, sensitivity, and several other indices. The best results were obtained with the combination of the RF classifier and the features selected by Relief achieving an accuracy of 98.36%. This could even further be improved by employing a 5-fold Cross Validation (CV) approach, resulting in an accuracy of 99.337%. 

Original languageEnglish
Title of host publicationICISDM 2021 - 5th International Conference on Information System and Data Mining
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Pages14-20
Number of pages7
ISBN (Electronic)9781450389549
DOIs
Publication statusPublished - 27 May 2021
Event5th International Conference on Information System and Data Mining, ICISDM 2021 - Virtual, Online, United States
Duration: 27 May 202129 May 2021

Publication series

NameACM International Conference Proceeding Series

Conference

Conference5th International Conference on Information System and Data Mining, ICISDM 2021
CountryUnited States
CityVirtual, Online
Period27/05/2129/05/21

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