Causal discovery approach with reinforcement learning for risk factors of type II diabetes mellitus

BMC Bioinformatics

Research

Jian-Gang Hu¹  Yun-Ming Wang¹  Xiu-E. Gao²  Sheng-Bin zhou²  Bo Chen³ 

[1] College of Automation and Electrical Engineering, Dalian Jiaotong University, 116028, Dalian, Liaoning, China;College of Computer Science and Intelligent Education, Lingnan Normal University, 524048, Zhanjiang, Guangdong, China;College of Electronic and Electrical Engineering, Lingnan Normal University, 524048, Zhanjiang, Guangdong, China;

关键词: Type 2 diabetes mellitus (T2DM);    Risk factors;    Reinforcement learning;   

DOI  :  10.1186/s12859-023-05405-x

 received in 2023-03-03, accepted in 2023-07-06,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundStatistical correlation analysis is currently the most typically used approach for investigating the risk factors of type 2 diabetes mellitus (T2DM). However, this approach does not readily reveal the causal relationships between risk factors and rarely describes the causal relationships visually.ResultsConsidering the superiority of reinforcement learning in prediction, a causal discovery approach with reinforcement learning for T2DM risk factors is proposed herein. First, a reinforcement learning model is constructed for T2DM risk factors. Second, the process involved in the causal discovery method for T2DM risk factors is detailed. Finally, several experiments are designed based on diabetes datasets and used to verify the proposed approach.ConclusionsThe experimental results show that the proposed approach improves the accuracy of causality mining between T2DM risk factors and provides new evidence to researchers engaged in T2DM prevention and treatment research.

【 授权许可】

CC BY   
© The Author(s) 2023

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