Integrating Data-Driven Insights for Heart Disease Prediction Using Machine Learning Algorithms

The World Health Organization reports that cardiovascular diseases constitute 32% of global deaths while staying as one of the top worldwide mortality factors. Heart disease detection during early stages enables us to decrease mortality numbers. The outcome quality of heart disease predictions advances through data-driven approaches employing machine learning protocols in this investigation project. The research examines structured data through XGBoost algorithm processing data derived from various research sources. Our dataset preparation demands the execution of feature selection methods to optimize model performance through modifications of hyper parameters. Our research performs algorithm evaluation through tests employing diagnostic criteria from the original study. The effectiveness of machine learning technology demonstrates its ability to deliver exact forecasting analytics that healthcare professionals need for their work. The combination of diagnostic tools leads to faster early detection and generates better medical care delivery results for patient health outcomes.