Abstract
Heart failure (HF) affects the health of millions of people worldwide and the early detection of HF risk plays a vital role in prevention and prompt treatment. Various decision support systems based on machine learning have been presented recently to predict HF. However, the existing systems usually assumed that all features add equal weight to the prediction result, which could not properly simulate the diagnostic status. In this study, a decision support system is proposed for HF prediction using MSE Back Propagation Method (MSEBPM) and weighted naive Bayes. First, the feature selection method eliminates irrelevant features to improve accuracy and decrease computational times. Second, the proposed MSEBPM computes a weight vector for features based on their contributions, trying to minimize the MSE loss of the predicted class probabilities. Finally, the trained weight vector is applied to the weighted naive Bayes model for HF risk prediction. The proposed system is evaluated with a published dataset of 899 patients, and compared with conventional data mining techniques and other state-of-the-art systems. The results show that our proposed system leads to 82.96% accuracy in HF risk prediction, which suggests that it could be used to early detect HF in the clinic.
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Acknowledgements
This work is supported by the Chinese Scientific and Technical Innovation Project 2030 (No. 2018AAA0102100), NSFC-General Technology Joint Fund for Basic Research (No. U1936206, No. U1836109), and National Natural Science Foundation of China (No. 61772289, No. U1903128, and No. 62002178).
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Song, K., Yu, S., Zhang, H., Zhang, Y., Cai, X., Yuan, X. (2021). A Decision Support System for Heart Failure Risk Prediction Based on Weighted Naive Bayes. In: Jensen, C.S., et al. Database Systems for Advanced Applications. DASFAA 2021. Lecture Notes in Computer Science(), vol 12683. Springer, Cham. https://doi.org/10.1007/978-3-030-73200-4_30
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