A Data-Driven Approach to Cardiac Health: Machine Learning for Heart Disease Prediction

Abstract

Heart disease remains one of the leading causes of death worldwide, underscoring the critical need for accurate and timely prediction models. The outcome of cardiac disease is one area where machine learning algorithms have shown substantial potential. A rapidly advancing area of research is focused on using machine learning for heart disease prediction. Recent studies have extensively explored machine learning methods to anticipate heart disease in patients. This research aims to develop precise prediction models that can identify individuals at high risk of developing heart disease. These models consider various characteristics such as age, gender, medical history, and lifestyle choices to calculate the likelihood of heart disease. Notably, the accuracy of these machine learning models often surpasses that of traditional methods used for predicting cardiac disease. Integrating machine learning algorithms into heart disease diagnosis and treatment can improve patient outcomes and overall health.

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