Predicting coronary heart disease in Chinese diabetics using machine learning.

Autor: Ma CY; School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 611731, China., Luo YM; School of Medical Information and Engineering, Southwest Medical University, Luzhou, 646000, China., Zhang TY; School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 611731, China., Hao YD; School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 611731, China., Xie XQ; School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 611731, China., Liu XW; School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 611731, China., Ren XL; Sichuan Chuanjiang Science and Technology Research Institute Co., Ltd, Luzhou, 646000, China., He XL; Sichuan Chuanjiang Science and Technology Research Institute Co., Ltd, Luzhou, 646000, China., Han YM; Beijing Physical Examination Center, Beijing, China., Deng KJ; School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 611731, China., Yan D; Beijing Institute of Clinical Pharmacy, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China., Yang H; School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China. Electronic address: huiyang@cuit.edu.cn., Tang H; School of Basic Medical Sciences, Southwest Medical University, Luzhou, 646000, China; Basic Medicine Research Innovation Center for Cardiometabolic Diseases, Ministry of Education, Luzhou, 646000, China. Electronic address: huatang@swmu.edu.cn., Lin H; School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 611731, China. Electronic address: hlin@uestc.edu.cn.
Jazyk: angličtina
Zdroj: Computers in biology and medicine [Comput Biol Med] 2024 Feb; Vol. 169, pp. 107952. Date of Electronic Publication: 2024 Jan 05.
DOI: 10.1016/j.compbiomed.2024.107952
Abstrakt: Diabetes, a common chronic disease worldwide, can induce vascular complications, such as coronary heart disease (CHD), which is also one of the main causes of human death. It is of great significance to study the factors of diabetic patients complicated with CHD for understanding the occurrence of diabetes/CHD comorbidity. In this study, by analyzing the risk of CHD in more than 300,000 diabetes patients in southwest China, an artificial intelligence (AI) model was proposed to predict the risk of diabetes/CHD comorbidity. Firstly, we statistically analyzed the distribution of four types of features (basic demographic information, laboratory indicators, medical examination, and questionnaire) in comorbidities, and evaluated the predictive performance of three traditional machine learning methods (eXtreme Gradient Boosting, Random Forest, and Logistic regression). In addition, we have identified nine important features, including age, WHtR, BMI, stroke, smoking, chronic lung disease, drinking and MSP. Finally, the model produced an area under the receiver operating characteristic curve (AUC) of 0.701 on the test samples. These findings can provide personalized guidance for early CHD warning for diabetic populations.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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