One ofthe most difficultissues inmedicine is predicting heartillness. Heart disease is becomingincreasingly common, and being able to predict such diseases in advance is critical. This is a challenging diagnosis that needs to be made correctly and swiftly. In this paper, we propose AutoML frameworks for predicting heart diseases, such as AutoGluon and H20. The dataset contains 919rows and12 columns withattributes such as Age, Sex, Chest Pain Type, RestingBP,Cholestrol, FastingBS, RestingECG, MaxHR, ExerciseAngina, Oldpeak, STSlope. While training the data AutoGluon uses LightGBM, RandomForset, and WeightedEnsemble provide certain accuracy and H20 uses XGBoost, GBM, and StackedEnsemble to provide certain accuracy among them H20 provides better results

Heart Disease, AutoML, Random Forest, WeightedEnsemble, StackedEnsemble,Prediction