Comparison of Machine Learning Algorithms for Heart Disease Prediction

Heart disease remains a significant health concern globally, prompting the exploration of advanced methodologies for its early prediction and intervention. In this research, we use a comprehensive method to predict heart disease by using the capability of various machine learning algorithms. A dataset comprising diverse patient attributes and medical indicators is utilized for training and testing the models. Our study explores the effectiveness of various prominent machine learning algorithms such as Random Forest, and K-Nearest Neighbors (KNN) in predicting heart disease risk. Through meticulous feature selection and engineering, we enhance the algorithm’s ability to discern patterns and relationships within the data. To improve model interpretability and generalizability, feature selection and optimization procedures are examined. Furthermore, a comparison between contrast the strengths and limitations of these algorithms to identify the most suitable model for heart disease prediction has been presented. The findings of this research contribute to the ongoing efforts to develop accurate and reliable predictive tools for early detection of cardiovascular diseases, thereby facilitating timely intervention and improving patient outcomes. Integration of machine learning into cardiovascular risk assessment holds great promise for advancing personalized medicine and preventive healthcare strategies.