Evaluating ensemble prediction of coronary heart disease using receiver operating characteristics

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.