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An ensemble framework for risk prediction of left atrial thrombus based on undersampling with replacement

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Abstract

Left atrial thrombus (LAT) impacts humans greatly and can result in ischemia and necrosis in severe cases. Therefore, health workers appeal to the social community to emphasize the importance of preventive treatment for LAT. This paper proposes an ensemble framework for risk prediction of LAT based on undersampling with replacement (EFRP-UR), addressing the problem of data imbalance. Firstly, in the feature selection process, we adopt the method of separately counting the essential features of data subsets. In view of the characteristics of class imbalance in medical data, we apply our improved undersamling method, “undersampling with replacement", to obtain a number of training subsets, train multiple base-classifiers, and use an iterative method to select the classifiers with better performance for subsequent integration, improving the prediction accuracy of the proposed EFRP-UR. To aim for disease risk prediction, we synthesize the results of different ensemble algorithms in the end to increase the recall rate. Applied to the LAT dataset obtained from the Regional Medical Center, our experimental results prove that the proposed EFRP-UR has improved in accuracy, recall rate and F1 value, compared with any single base-classifier. In addition, if comprehensive data on other diseases exist, EFRP-UR can also be transferred to predict other diseases.

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Data availability

Our dataset is provided by Department of Cardiovascular Medicine, Renmin Hospital of Wuhan University. It can be made available on reasonable request.

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Authors and Affiliations

  1. School of Electronic Information, Wuhan University, Wuhan, 430072, China

    Li Li, Dongshen Fang, Qiyao Ye & Tan Hu

  2. Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, 430060, China

    Shaobo Shi

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  1. Li Li
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Li, L., Fang, D., Ye, Q. et al. An ensemble framework for risk prediction of left atrial thrombus based on undersampling with replacement. Neural Comput & Applic 36, 18613–18625 (2024). https://doi.org/10.1007/s00521-024-10166-6

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