Risk assessment of mild cognitive impairment in elderly patients with diabetes mellitus based on machine learning

ZHANG Haixin; ZHANG Yifang; XIE Zhilan; ZHANG Wenling; WANG Yuping; LI Jinlei

doi:10.16462/j.cnki.zhjbkz.2024.03.006

Volume 28 Issue 3

Mar. 2024

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ZHANG Haixin, ZHANG Yifang, XIE Zhilan, ZHANG Wenling, WANG Yuping, LI Jinlei. Risk assessment of mild cognitive impairment in elderly patients with diabetes mellitus based on machine learning[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(3): 284-289. doi: 10.16462/j.cnki.zhjbkz.2024.03.006

Citation:

ZHANG Haixin, ZHANG Yifang, XIE Zhilan, ZHANG Wenling, WANG Yuping, LI Jinlei. Risk assessment of mild cognitive impairment in elderly patients with diabetes mellitus based on machine learning[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(3): 284-289. doi: 10.16462/j.cnki.zhjbkz.2024.03.006

Citation:

ZHANG Haixin, ZHANG Yifang, XIE Zhilan, ZHANG Wenling, WANG Yuping, LI Jinlei. Risk assessment of mild cognitive impairment in elderly patients with diabetes mellitus based on machine learning[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(3): 284-289. doi: 10.16462/j.cnki.zhjbkz.2024.03.006

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Risk assessment of mild cognitive impairment in elderly patients with diabetes mellitus based on machine learning

doi: 10.16462/j.cnki.zhjbkz.2024.03.006

Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China

Funds:

China Medical Board Program CMB 22-467

More Information

Corresponding author: WANG Yuping, E-mail: wyp@pumc.edu.cn; LI Jinlei, E-mail: lijinlei@sph.pumc.edu.cn
Received Date: 2023-09-07
Rev Recd Date: 2024-01-04

Available Online: 2024-04-08

Publish Date: 2024-03-10

Abstract

Abstract

Objective This study aims to develop a high-accuracy risk assessment model for identifying the risk of mild cognitive impairment (MCI) in elderly patients with diabetes mellitus using machine learning algorithms, providing insights for early identification and prevention of cognitive impairment in this population. Methods A total of 1 319 patients aged 60 and above with type 2 diabetes mellitus, who visited the Endocrinology Department of People′s Hospital of Penglai in Yantai City, Shandong Province, between October 2021 and May 2022, were enrolled as the study population. The demographic information, medical history, lifestyle factors, psychological health status, and physiological indicators were collected. The Montreal Cognitive Assessment (MoCA) scale was used to evaluate the cognitive function of patients. BP neural network model, random forest model, and XGBoost model were constructed using R version 4.1.3 software. The accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and the area under the curve (AUC) with 95% CI of models were calculated. Results The sensitivity values of the BP neural network model, random forest model, and XGBoost model were 57.79%, 77.89%, and 80.40%, respectively. The specificity values were 78.17%, 60.41%, and 61.42% for the respective models. The AUC values for the ROC curves were 0.746 (95% CI: 0.698-0.794), 0.755 (95% CI: 0.708-0.802), and 0.756 (95% CI: 0.709-0.803), respectively. Conclusions The XGBoost model and random forest model demonstrated good performance and showed potential for application in the field of MCI risk assessment among elderly patients with diabetes mellitus.
- Diabetes mellitus,
- Mild cognitive impairment,
- Back propagation neural network,
- Random forest,
- XGBoost

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References(14)

References

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