<![CDATA[This research focuses on the development of an early detection system for heart disease using Artificial Intelligence (AI) and the Random Forest method. Heart disease, particularly coronary heart disease (CHD), is a leading cause of death worldwide, with early detection being a crucial factor in reducing mortality rates. The study uses a dataset from the UCI Machine Learning Repository, consisting of 303 patient records with various medical factors like age, sex, cholesterol levels, and blood pressure. The Random Forest algorithm is employed to create a predictive model capable of classifying whether an individual is at risk for heart disease. Data preprocessing, including normalization and encoding categorical variables, is carried out before training the model. The system achieved a high accuracy rate of 98.5% in predicting heart disease risk. The developed model is deployed as an API and can be integrated into healthcare applications for real-time risk prediction, supporting timely medical decisions. This system aims to contribute to the improvement of early heart disease detection, offering an efficient tool for healthcare professionals.]]>
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