Published Paper Details:

Heart disease remains a pervasive global health concern, maintaining its status as a primary cause of mortality. Early detection is pivotal for effective intervention strategies. This research leverages the Random Forest algorithm to predict the risk of heart disease, utilizing a comprehensive dataset encompassing patient attributes such as age, gender, cholesterol levels, blood pressure, and other pertinent clinical features. Renowned for its robustness and accuracy, the Random Forest model undergoes training on historical data to discern intricate patterns and relationships within the dataset. Through a meticulous evaluation process, we showcase the model's proficiency in accurately categorizing individuals into heart disease risk groups. This underscores its significance as a valuable tool for healthcare professionals in the assessment of patient cardiovascular health. This study represents a meaningful contribution to the ongoing endeavors aimed at harnessing machine learning to enhance heart disease prevention and elevate the standard of patient care.