What does this research mean for the field?

An ensemble machine learning model for predicting coronary heart disease achieves an accuracy of 94.21% and a ROC of 0.981, significantly outperforming individual algorithms such as K-Nearest Neighbor, Artificial Neural Networks, and Support Vector Machines. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

September 1, 2017

Evaluating ensemble prediction of coronary heart disease using receiver operating characteristics

Q: What is the clinical evidence from this study?

Study design: Other. Population: Coronary heart disease. Intervention: Ensemble Model vs. K-Nearest Neighbor, Artificial Neural Networks and Support Vector Machines. Primary outcome: Model performance (Accuracy, ROC, RMSE, Precision) (ROC 0.981).

Key Result

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.

Structured PICO

Does an Ensemble Model improve the prediction of heart disease compared to standard machine learning algorithms?

Population

Datasets containing records of diverse heart diseases

Intervention

Proposed Ensemble Model exploiting pertinent machine learning procedures

Comparator

K-Nearest Neighbor, Artificial Neural Networks and Support Vector Machines algorithms

Outcome

Categorization of diverse heart diseases evaluated by accuracy, ROC, RMSE, and Precisionsurrogate

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.

Main Result

Effect estimate: ROC 0.981

Abstract

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.

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Cite This Study

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.

synapsesocial.com/papers/6a16391cf9004307dec1fd63 https://doi.org/https://doi.org/10.1109/itecha.2017.8101920

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