A REVIEW ON MACHINE LEARNING MODEL FOR AUTOMATIC HEART DISEASE PREDICTION

Abstract: Heart disease is a leading cause of mortality worldwide, necessitating early detection and prevention strategies. Machine learning (ML) models have emerged as powerful tools for automatic heart disease prediction. This review paper provides an overview of the recent advancements in ML-based approaches for heart disease prediction. We begin by discussing the significance of early detection and the potential of ML in this domain. Next, we conduct a comprehensive literature survey, summarizing the key findings from previous studies. We then present a comparative study of various ML algorithms commonly used for heart disease prediction, highlighting their strengths and limitations. Additionally, we outline the proposed procedure for developing ML models for heart disease prediction and discuss potential future directions. Finally, we conclude by emphasizing the importance of continued research in this area to improve the accuracy and accessibility of automatic heart disease prediction.

Keywords: Classification, Segmentation, Image Processing, Skin Cancer, Machine Learning, Heart Diseases, Feature Extraction.