Development of a machine learning-based risk prediction model for mild cognitive impairment with spleen-kidney deficiency syndrome in the elderly.
10.1016/j.joim.2025.06.005
- Author:
Ya-Ting AI
1
;
Shi ZHOU
2
;
Ming WANG
3
;
Tao-Yun ZHENG
1
;
Hui HU
4
,
5
;
Yun-Cui WANG
6
;
Yu-Can LI
2
;
Xiao-Tong WANG
2
;
Peng-Jun ZHOU
2
Author Information
1. School of Nursing, Hubei University of Chinese Medicine, Wuhan 430065, Hubei Province, China; Hubei Shizhen Laboratory, Hubei University of Chinese Medicine, Wuhan 430061, Hubei Province, China; Engineering Research Center of TCM Protection Technology and New Product Development for the Elderly Brain Health, Ministry of Education, Hubei University of Chinese Medicine, Wuhan 430065, Hubei Province, China.
2. School of Nursing, Hubei University of Chinese Medicine, Wuhan 430065, Hubei Province, China.
3. Traditional Chinese Medicine Department, Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan 430072, Hubei Province, China.
4. School of Nursing, Hubei University of Chinese Medicine, Wuhan 430065, Hubei Province, China; Hubei Shizhen Laboratory, Hubei University of Chinese Medicine, Wuhan 430061, Hubei Province, China; Engineering Research Center of TCM Protection Technology and New Product Development for the Elderly Brain Health, Ministry of Education, Hubei University of Chinese Medicine, Wuhan 430065, Hubei Province, China. Electronic address: zhongyi90@
5. com.
6. School of Nursing, Hubei University of Chinese Medicine, Wuhan 430065, Hubei Province, China; Hubei Shizhen Laboratory, Hubei University of Chinese Medicine, Wuhan 430061, Hubei Province, China; Engineering Research Center of TCM Protection Technology and New Product Development for the Elderly Brain Health, Ministry of Education, Hubei University of Chinese Medicine, Wuhan 430065, Hubei Province, China. Electronic address: yuncui_wang@hbtcm.edu.cn.
- Publication Type:Journal Article
- Keywords:
Machine learning;
Mild cognitive impairment;
Risk factors;
Spleen-kidney deficiency syndrome;
Traditional Chinese medicine
- MeSH:
Humans;
Cognitive Dysfunction/diagnosis*;
Aged;
Male;
Female;
Machine Learning;
Spleen;
Aged, 80 and over;
Kidney;
Medicine, Chinese Traditional
- From:
Journal of Integrative Medicine
2025;23(4):390-397
- CountryChina
- Language:English
-
Abstract:
OBJECTIVE:As an age-related neurodegenerative disease, the prevalence of mild cognitive impairment (MCI) increases with age. Within the framework of traditional Chinese medicine, spleen-kidney deficiency syndrome (SKDS) is recognized as the most frequent MCI subtype. Due to the covert and gradual onset of MCI, in community settings it poses a significant challenge for patients and their families to discern between typical aging and pathological changes. There exists an urgent need to devise a preliminary diagnostic tool designed for community-residing older adults with MCI attributed to SKDS (MCI-SKDS).
METHODS:This investigation enrolled 312 elderly individuals diagnosed with MCI, who were randomly distributed into training and test datasets at a 3:1 ratio. Five machine learning methods, including logistic regression (LR), decision tree (DT), naive Bayes (NB), support vector machine (SVM), and gradient boosting (GB), were used to build a diagnostic prediction model for MCI-SKDS. Accuracy, sensitivity, specificity, precision, F1 score, and area under the curve were used to evaluate model performance. Furthermore, the clinical applicability of the model was evaluated through decision curve analysis (DCA).
RESULTS:The accuracy, precision, specificity and F1 score of the DT model performed best in the training set (test set), with scores of 0.904 (0.845), 0.875 (0.795), 0.973 (0.875) and 0.973 (0.875). The sensitivity of the training set (test set) of the SVM model performed best among the five models with a score of 0.865 (0.821). The area under the curve of all five models was greater than 0.9 for the training dataset and greater than 0.8 for the test dataset. The DCA of all models showed good clinical application value. The study identified ten indicators that were significant predictors of MCI-SKDS.
CONCLUSION:The risk prediction index derived from machine learning for the MCI-SKDS prediction model is simple and practical; the model demonstrates good predictive value and clinical applicability, and the DT model had the best performance. Please cite this article as: Ai YT, Zhou S, Wang M, Zheng TY, Hu H, Wang YC, Li YC, Wang XT, Zhou PJ. Development of a machine learning-based risk prediction model for mild cognitive impairment with spleen-kidney deficiency syndrome in the elderly. J Integr Med. 2025; 23(4): 390-397.
