Heart Disease Prediction using Machine Learning

Heart disease (HD) remains a leading cause of death worldwide, making early detection essential for reducing mortality rates.Traditional diagnostic approaches often lack precision and are not always feasible for continuous monitoring.This project presents a machine learning-based heart disease prediction system utilizing the Random Forest (RF) algorithm, a robust and efficient ensemble learning technique.Random Forest operates by constructing multiple decision trees during training and aggregating their outputs to enhance predictive accuracy, minimize over fitting, and increase model stability.The model is applied to a medical dataset comprising vital patient features such as age, cholesterol levels, blood pressure, and electrocardiogram (ECG) readings.Data preprocessing steps include handling missing values and normalizing features to ensure optimal model performance.The RF classifier is trained and evaluated based on key metrics such as accuracy and precision.Leveraging RF's inherent feature selection capabilities and its ensemble-based decision-making, the system achieves high prediction accuracy.This predictive model assists healthcare professionals in early diagnosis and risk assessment of heart disease, facilitating timely medical interventions and improving patient outcomes.The results highlight the potential of Random Forest in developing a reliable and interpretable tool for cardiovascular risk prediction and clinical decision support.