Heart Disease Detection System using Machine Learning

Heart failure is a complex condition impacting a large population globally. Early detection is critical. The early identification of heart disease is a major priority in healthcare. The first method utilizes a weighted Naïve Bayes approach for predicting heart disease. A third approach involves using an enhanced SVM, optimized through duality, to automatically identify heart failure. Furthermore, the paper examines a clinical choice bolster framework (CDSS) that leverages a heart malady expectation show. The point of this ponder is to prepare clinicians with apparatuses for the early determination of heart illness, empowering more compelling treatment and the anticipation of genuine wellbeing issues. The ponder compares four machine learning models, centering on progressing the precision of heart malady conclusion through the utilize of XGBoost and different choice tree classification calculations.