Heart Disease Prediction Using Random Forest Algorithm: A Comprehensive Analysis

Abstract

Heart disease remains a leading cause of mortality worldwide. Early prediction and diagnosis are crucial for effective treatment and management. This study employs the Random Forest algorithm to predict heart disease using a dataset comprising various clinical features. The methodology includes data preprocessing, feature encoding, model training, and evaluation. The model's performance is assessed using accuracy metrics and a confusion matrix. The results demonstrate the efficacy of the Random Forest algorithm in predicting heart disease, highlighting its potential as a reliable tool in clinical decision support systems.