HEART DISEASE PREDICTION

Abstract

Heart disease continues to be a leading cause of global morbidity and mortality and is promoted by the prevalence of lifestyle-related risk factors, which includes unhealthy diet, physical inactivity, and stress. Effective prediction and prevention strategies are important for this. This paper reviews current state-of-the-art methodologies on predicting heart disease, covering clinical assessment, statistical modeling, and machine learning techniques. Each of the methods is critically appraised for its strengths and limitations, highlighting their individual contributions to diagnostic accuracy and early intervention.

Clinical approaches depend on traditional risk factors, including blood pressure, cholesterol, and family history, but fail at the individual level. Statistical models, such as regression analyses, provide information for population-level trends but could be too simplistic to deal with complex interactions. Machine learning (ML) models, in comparison, have an unparalleled ability to handle large datasets, discover non-linear patterns, and adapt to dynamic environments. Major challenges remain in data quality, feature selection, and interpretability.

The study underlines the importance of high- quality, diverse datasets and ethical considerations, particularly concerning equity and minimizing bias in predictive models. A holistic framework combining clinical expertise with robust statistical methods and ML innovations.