Novel Method of Characterization of Heart Disease Prediction Using Sequential Feature Selection-Based Ensemble Technique

The exact forecast of heart disease is necessary to proficiently treating cardiovascular patients before a failure happens. Assuming we talk about artificial intelligence (AI) techniques, can be accomplished utilizing an ideal AI model with rich medical services information on diseases. To begin with, the feature extraction technique, gradient boosting-based sequential selection (GBSFS) applied separate significant number features from coronary illness dataset create important information. Using machine learning algorithms like Decision tree (DT), Random forest (RF), Multilayer perceptron (MLP), Support vector (SVM), Extra (ET), Gradient boosting (GBC), Linear regression (LR), K-nearest neighbor (KNN), and stacking, comparison created between that include both all features. With proposed framework achieves test accuracy 98.78 percent, which higher than existing frameworks most notable in marking 11 This outcome shows our more powerful for expectation illness, contrast other cutting edge strategies.