Heart disease diagnosis Performance evaluation of supervised machine learning and feature selection techniques

Abstract

Heart disease diagnosis is a critical task in modern healthcare, demanding accurate and efficient methods. This study focuses on evaluating the performance of various supervised machine learning algorithms and feature selection techniques for the diagnosis of heart disease. The algorithms considered include Naïve Bayes, Decision Tree, KNN, SVM, and Logistic Regression. Additionally, different feature selection techniques are employed to identify the most relevant features for improved diagnostic accuracy. Furthermore, the study investigates the impact of feature selection on algorithm performance. Feature selection techniques are applied to identify the subset of attributes that contribute most effectively to heart disease diagnosis. The combination of various algorithms and feature selection methods yields insights into which approaches are most suitable for accurate and efficient heart disease diagnosis