Optimizing Heart Disease Prediction with Random Forest: Insights from the Kaggle Dataset

In this work, a dataset of cardiovascular health markers is used to investigate the use of different machine learning models to heart disease prediction. Performance of several algorithms—Logistic Regression, Decision Trees, Random Forest, and Support Vector Machines—is compared in this work. Using methods like cross-validation and hyperparameter tuning, the research seeks to find the most precise and effective model. The findings show that [best performing model] provides a strong instrument for early heart disease diagnosis by achieving the highest prediction accuracy. This paper demonstrates how machine learning may be used to improve patient outcomes and diagnostic accuracy in cardiovascular healthcare.