Effective Heart Disease Prediction Using Machine Learning Techniques

The diagnosis and prognosis of cardiovascular disease are crucial medical tasks to ensure correct classification, which helps cardiologists provide proper treatment the patient. Machine learning applications in niche have increased as they can recognize patterns from data. Using machine classify occurrence help diagnosticians reduce misdiagnosis. This research develops a model that correctly predict diseases fatality caused by diseases. paper proposes method k-modes clustering with Huang starting improve classification accuracy. Models such random forest (RF), decision tree classifier (DT), multilayer perceptron (MP), XGBoost (XGB) used. GridSearchCV was used hypertune parameters applied optimize result. proposed is real-world dataset 70,000 instances Kaggle. were trained on data split 80:20 achieved accuracy follows: tree: 86.37% (with cross-validation) 86.53% (without cross-validation), XGBoost: 86.87% 87.02% forest: 87.05% 86.92% perceptron: 87.28% 86.94% cross-validation). models AUC (area under curve) values: 0.94, 0.95, 0.95. conclusion drawn this underlying cross-validation has outperformed all other algorithms terms It highest 87.28%.