@inproceedings{d7818d312610420e858a6ddee8223954,
title = "Heart Disease Prediction using Machine Learning Techniques",
abstract = "One of the main contributors to death cases globally is heart diseases. Heart illnesses have an impact on many people in the middle or elderly age which, in most instances, lead to serious health adverse effects such as strokes and heart attacks. Therefore, it is necessary to diagnose and predict heart diseases to prevent any serious health issues before they occur. In this paper, a provisional study and examination, using different state of the art Machine Learning Techniques namely Artificial Neural Networks, Decision Trees and Na{\"i}ve Bayes, Random Forest, Logistic Regression, Support Vector Machines and XG Boost, were implemented at various evaluation stages to predict heart diseases. Results show that Random Forest technique has outperformed the other techniques and achieved a prediction accuracy of 95%.",
keywords = "Decision Trees, Heart Disease, Machine Learning, Naive Bayes, Neural Networks",
author = "Reldean Williams and Thokozani Shongwe and Hasan, {Ali N.} and Vikash Rameshar",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021 ; Conference date: 25-10-2021 Through 26-10-2021",
year = "2021",
doi = "10.1109/ICDABI53623.2021.9655783",
language = "English",
series = "2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "118--123",
booktitle = "2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021",
address = "United States",
}