HEART DISEASE DETECTION USING RANDOM FOREST

Abstract: Heart disease remains a significant global health challenge, contributing to substantial morbidity and mortality rates. Early identification of individuals at risk of developing heart disease is crucial for implementing preventive measures and improving patient outcomes. In recent years, machine learning techniques have emerged as powerful tools for predicting heart disease risk by analysing various clinical and demographic factors. In this study, we investigate the efficacy of the Random Forest Classifier, an ensemble learning algorithm, in predicting heart disease risk. The study leverages a comprehensive dataset containing demographic information, clinical measurements, and lifestyle factors collected from diverse sources such as electronic health records and surveys. Keyword: Heart disease, Risk prediction, Random Forest Classifier, Machine learning, Ensemble learning, Predictive modelling, Feature engineering, Data preprocessing, Clinical decision-making, Healthcare