a comparative study of machine learning algorithm for heart disease prediction

Heart disease continues to be a major public health challenge and one of the leading causes of mortality worldwide.Early prediction and diagnosis play a vital role in reducing risks and improving the quality of life for patients.This research paper focuses on the analysis of a clinical dataset containing 303 patient records with 14 key medical attributes such as age, sex, chest pain type, cholesterol level, blood pressure, and maximum heart rate.The study applies exploratory data analysis and correlation-based evaluation to identify the most influential risk factors associated with heart disease.A conceptual methodology framework is presented through a diagram to explain the step-by-step research approach, while avoiding algorithmic or implementation-level complexity.Results from the statistical analysis highlight that variables including age, chest pain type, cholesterol level, and exercise-induced angina have strong associations with the presence of heart disease.Visual representations such as histograms, distribution graphs, and a correlation heatmap provide further clarity on data trends.This research contributes to academic learning by offering a structured approach to dataset-driven health analysis, and it sets the foundation for future studies that can apply machine learning and predictive modeling for improved accuracy in heart disease prediction.