An Ensemble Model for predicting coronary heart disease achieved an accuracy of 94.21% and a ROC of 0.981, outperforming K-Nearest Neighbor, Artificial Neural Networks, and Support Vector Machines.
Does an Ensemble Model improve the prediction of heart disease compared to standard machine learning algorithms?
The proposed ensemble machine learning model achieves high accuracy (94.21%) and ROC (0.981) in predicting heart disease, outperforming standard algorithms like KNN, ANN, and SVM.
Effect estimate: ROC 0.981
Heart diseases may perhaps consequence in debility, severe disorder, and meager quality of lifespan. Furthermore, it could also be lethal. Hence inferring heart disease has turn into foremost distress currently. This paper centers on various machine learning practices which assist ascertaining and perceiving innumerable heart diseases. Multifarious machine learning approaches conversed here are Hidden Markov Models, Support Vector Machine, Feature Selection, Computational intelligent classifier, prediction system, data mining techniques and genetic algorithm. Scrutinizing each approach thoroughly allowed us to select most apposite one. This ultimately permits us to propose an Ensemble Model exploiting pertinent machine learning procedures which perfectly categorizes diverse heart diseases. The evaluation of the proposed technique has been conducted using state of the art technology. The proposed technique has an accuracy of 94.21%, a ROC (Receiver Operating Characteristics) of 0.981, RMSE (Root Mean Square Error) of .2568, Precision of 0.953; showing significant improvement when compared to the performance of K-Nearest Neighbor, Artificial Neural Networks and Support Vector Machines algorithms. Analysis/Evaluation of the implemented algorithms and the proposed Ensemble Model has been done expending the Receiver Operator Characteristics.
Mahboob et al. (Fri,) conducted a other in Coronary heart disease. Ensemble Model vs. K-Nearest Neighbor, Artificial Neural Networks and Support Vector Machines was evaluated on Model performance (Accuracy, ROC, RMSE, Precision) (ROC 0.981). An Ensemble Model for predicting coronary heart disease achieved an accuracy of 94.21% and a ROC of 0.981, outperforming K-Nearest Neighbor, Artificial Neural Networks, and Support Vector Machines.