Analyzing Machine Learning Algorithms for Predicting Heart Disease

Abstract

In recent years, cardiovascular diseases have become a leading cause of global mortality, attributed to factors such as shifts in lifestyle, dietary patterns, and work cultures. This pervasive issue affects individuals worldwide, spanning developed, underdeveloped, and developing nations. Detecting early signs of cardiovascular diseases and providing consistent medical monitoring are crucial in mitigating the escalating number of patients and lowering mortality rates. However, challenges arise due to limited medical resources and a shortage of specialist doctors, hindering continuous patient monitoring and consultations. To address this, technological interventions are essential to facilitate remote patient monitoring and treatment. Leveraging healthcare data generated from various medical procedures and continuous monitoring can lead to the development of effective prediction models for cardiovascular diseases. Early prognosis enables timely interventions, guiding lifestyle changes in high-risk individuals and ultimately reducing complications, marking a significant milestone in the field of medicine. This paper explores commonly used machine learning algorithms for predicting heart diseases by utilizing medical data and historical information. The study delves into techniques such as KNN, Decision Tree, Gaussian Naïve Bayes, Logistic Regression, and Random Forest, offering a comparative analysis of their effectiveness. Additionally, the report discusses the advantages and disadvantages of employing these techniques in developing prediction models.