Deep confidence network-based prediction of microvascular complications in type 2 diabetes mellitus

Corresponding author: wutianxing, tianxingwu@seu.edu.cn

DOI: 10.12201/bmr.202404.00021

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  Abstract: OBJECTIVE: Diabetic microvascular complications have an insidious onset and are easily overlooked, thus causing irreversible and substantial damage; therefore, early screening and prediction of diabetic microvascular lesions are of great significance. METHODS: Using real-world data from a tertiary hospital over a period of 10 years, we constructed a particle swarm algorithm optimized deep confidence network-based prediction model for microvascular complications in type 2 diabetes mellitus (PSO-DBN) by taking test results and medical record documents into consideration. RESULTS: The PSO-DBN model prediction results can realize the prediction of diabetic microvascular complications, and the performance is better than that of RF and SVM benchmark models, which improves the performance of microvascular complications classification prediction of type 2 diabetes mellitus under the real-world data to a certain extent, and provides reference for the research of disease prediction model of real-world data.

  Key words: type 2 diabetes; microvascular complications; disease prediction models; clinical data processing; real-world data

  Submit time: 11 April 2024

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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  1	2024-01-03	bmr.202404.00021V1	Download

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