Abstract
Clinical decision support systems have always assisted physicians in diagnosing diseases. Coronary artery disease (CAD) is currently responsible for a large percentage of deaths, which motivated researchers to propose more accurate prediction models. This paper employs neural networks (NN) and a boosted C5.0 decision tree model to predict CAD for the well-known Cleveland Heart Disease dataset. We attempt to tune the optimal size and configuration of the neural networks and identify the insensitive features in both models, followed by assessing the effect of eliminating such features in the results. Both models are evaluated through ten experiments, each of which has different training and testing datasets, but with the same size. The most and the least important input features in each model are determined. The performance of the reduced dataset, i.e., the removed insignificant features contributing to the models, has been evaluated through statistical tests. Our results show that there is no significant difference between running the NN and C5.0 algorithms by initial dataset in terms of three performance criteria: positive prediction value (PPV), negative prediction value (NPV) and total accuracy value (TAV). Regarding the TAV criterion, the NN applied to the reduced dataset outperforms the C5.0 model with a 95% confidence interval. Finally, further discussion shows the trade-off between the NPV and PPV.
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Ahmadi, E., Weckman, G.R. & Masel, D.T. Decision making model to predict presence of coronary artery disease using neural network and C5.0 decision tree. J Ambient Intell Human Comput 9, 999–1011 (2018). https://doi.org/10.1007/s12652-017-0499-z
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DOI: https://doi.org/10.1007/s12652-017-0499-z