research-article

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

Authors:

Mirjam Jonkman,

MD. Zahid HasanAuthors Info & Claims

ICISDM '21: Proceedings of the 2021 5th International Conference on Information System and Data Mining

Pages 14 - 20

https://doi.org/10.1145/3471287.3471297

Published: 25 September 2021 Publication History

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%.

CCS CONCEPTS • Applied computing • Life and medical sciences • Health informatics

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ICISDM '21: Proceedings of the 2021 5th International Conference on Information System and Data Mining

May 2021

162 pages

ISBN:9781450389549

DOI:10.1145/3471287

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 25 September 2021

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ICISDM 2021: 2021 the 5th International Conference on Information System and Data Mining

May 27 - 29, 2021

CA, Silicon Valley, USA

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https://doi.org/10.1109/ACCESS.2025.3525514
Bai TXu MZhang TJia XWang FJiang XWei X(2025)Exploration and comparison of the effectiveness of swarm intelligence algorithm in early identification of cardiovascular diseaseScientific Reports10.1038/s41598-025-87598-015:1Online publication date: 7-Feb-2025
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https://doi.org/10.1109/ic-ETITE58242.2024.10493410
Bansla VSaini R(2024)Utilizing a Machine Learning Approach, Chronic Disease Identification and Prediction2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE)10.1109/IC3SE62002.2024.10592902(815-819)Online publication date: 9-May-2024
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Qi TZaki F(2024)Optimizing Heart Disease Prediction: Integrating Relief and Logistic Regression for Feature Selection2024 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS)10.1109/AiDAS63860.2024.10730361(24-29)Online publication date: 3-Sep-2024
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