Predictive Modeling for Heart Disease Detection using Machine Learning Algorithms

Machine Learning is a state-of-art technology applied widely in various sectors including health care and has made significant advancements in this field by revolutionizing diagnostic and treatment approaches. Cardiovascular diseases (CVD) have remained a leading global cause of death witnessed across the world. However, early detection of this disease has proven crucial for improving patient health outcomes. In this context, the current study has investigated the use of machine learning models to predict cardiovascular disease risk by examining clinical and lifestyle factors, including age, gender, blood pressure, cholesterol and glucose levels, smoking habits, and alcohol consumption. Six different machine learning models are trained and tested over the available data from Kaggle. The built models are evaluated based on various performance metrics including accuracy score, training time, F1-Score, recall, etc. The final inference of the study is that the decision tree models, particularly, random forest and gradient boosting algorithms proved to be more accurate among all other models used in this study. The research emphasized how these machine learning techniques have improved early diagnosis and facilitated timely interventions, helping healthcare providers make more informed decisions and ultimately enhanced patient care.