Heart Disease Prediction and Analysis using various Machine Learning Algorithms

• Author(s): Soumya Tripathi; Tanya Verma; Sunny; Harsh
• Paper ID: 1719790
• Page: 567-572
• Published Date: 31-03-2023
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 6 Issue 9 March-2023
• DOI: https://doi.org/10.64388/IREV6I9-1719790

Abstract

Despite numerous advancements in modern medicine, cardiovascular diseases still rank among the top mortality factors all around the world, with 18 million annual deaths caused by heart diseases according to the World Health Organization estimates. Identification of individuals with elevated risk of heart diseases is important in order to reduce the mortality rate especially in developing countries with limited access to medical facilities. Thanks to recent progress made in Artificial Intelligence and Machine Learning, it became possible to create intelligent diagnostic systems that can help doctors diagnose patients at an early stage. This paper aims at evaluating the performance of five supervised machine learning algorithms for heart disease prediction, namely, Random Forest, Support Vector Machine, Logistic Regression, Decision Tree, and K-Nearest Neighbors. Based on experimental evaluation, the Random Forest algorithm shows the best performance providing an accuracy of 90.16% while logistic regression shows accuracy of 85.25%, and the KNN classifier shows the lowest accuracy level of 67.21%. In addition to classification accuracy, the precision and recall values are also estimated based on the confusion matrix. The achieved results suggest that ensembles of learning algorithms provide better predictions while maintaining appropriate level of generalization.

Keywords

Random Forest, Decision Tree, SVM, Machine Learning, Heart Disease, KNN, Logistic Regression

Citations

IRE Journals:
Soumya Tripathi, Tanya Verma, Sunny, Harsh "Heart Disease Prediction and Analysis using various Machine Learning Algorithms" Iconic Research And Engineering Journals Volume 6 Issue 9 2023 Page 567-572 https://doi.org/10.64388/IREV6I9-1719790

IEEE:
Soumya Tripathi, Tanya Verma, Sunny, Harsh "Heart Disease Prediction and Analysis using various Machine Learning Algorithms" Iconic Research And Engineering Journals, vol. 6, no. 9, Mar. 2023, doi: https://doi.org/10.64388/IREV6I9-1719790

APA:
Soumya Tripathi, Tanya Verma, Sunny, Harsh (2023). Heart Disease Prediction and Analysis using various Machine Learning Algorithms. Iconic Research And Engineering Journals, 6(9). doi: https://doi.org/10.64388/IREV6I9-1719790

MLA:
Soumya Tripathi, Tanya Verma, Sunny, Harsh "Heart Disease Prediction and Analysis using various Machine Learning Algorithms" Iconic Research And Engineering Journals, vol. 6, no. 9, Mar. 2023. Crossref, https://doi.org/10.64388/IREV6I9-1719790

BibTeX

@article{1719790,
author = {Soumya Tripathi, Tanya Verma, Sunny, Harsh},
title = {Heart Disease Prediction and Analysis using various Machine Learning Algorithms},
journal = {Iconic Research And Engineering Journals},
year = {2023},
volume = {6},
number = {9},
pages = {567-572},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1719790.pdf},
abstract = {Despite numerous advancements in modern medicine, cardiovascular diseases still rank among the top mortality factors all around the world, with 18 million annual deaths caused by heart diseases according to the World Health Organization estimates. Identification of individuals with elevated risk of heart diseases is important in order to reduce the mortality rate especially in developing countries with limited access to medical facilities. Thanks to recent progress made in Artificial Intelligence and Machine Learning, it became possible to create intelligent diagnostic systems that can help doctors diagnose patients at an early stage. This paper aims at evaluating the performance of five supervised machine learning algorithms for heart disease prediction, namely, Random Forest, Support Vector Machine, Logistic Regression, Decision Tree, and K-Nearest Neighbors. Based on experimental evaluation, the Random Forest algorithm shows the best performance providing an accuracy of 90.16% while logistic regression shows accuracy of 85.25%, and the KNN classifier shows the lowest accuracy level of 67.21%. In addition to classification accuracy, the precision and recall values are also estimated based on the confusion matrix. The achieved results suggest that ensembles of learning algorithms provide better predictions while maintaining appropriate level of generalization.},
keywords = {Random Forest, Decision Tree, SVM, Machine Learning, Heart Disease, KNN, Logistic Regression},
month = {March}
}