Heart Disease Prediction using Machine Learning Techniques
Reldean Williams, Thokozani Shongwe, Ali N. Hasan, Vikash Rameshar
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 Naïve 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%.