Machine and deep learning-based clinical characteristics and laboratory markers for the prediction of sarcopenia.
10.1097/CM9.0000000000002633
- Author:
He ZHANG
1
;
Mengting YIN
1
;
Qianhui LIU
1
;
Fei DING
1
;
Lisha HOU
2
;
Yiping DENG
2
;
Tao CUI
3
;
Yixian HAN
3
;
Weiguang PANG
4
;
Wenbin YE
5
;
Jirong YUE
2
;
Yong HE
1
Author Information
1. Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
2. Department of Geriatrics and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
3. Department of Gynecology and Obstetrics, West China Second University Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
4. Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning 110819, China.
5. Department of Geriatrics, Xiamen Hospital of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Xiamen, Fujian 361015, China.
- Publication Type:Journal Article
- MeSH:
Humans;
Aged;
Sarcopenia/diagnosis*;
Deep Learning;
Aging;
Algorithms;
Biomarkers
- From:
Chinese Medical Journal
2023;136(8):967-973
- CountryChina
- Language:English
-
Abstract:
BACKGROUND:Sarcopenia is an age-related progressive skeletal muscle disorder involving the loss of muscle mass or strength and physiological function. Efficient and precise AI algorithms may play a significant role in the diagnosis of sarcopenia. In this study, we aimed to develop a machine learning model for sarcopenia diagnosis using clinical characteristics and laboratory indicators of aging cohorts.
METHODS:We developed models of sarcopenia using the baseline data from the West China Health and Aging Trend (WCHAT) study. For external validation, we used the Xiamen Aging Trend (XMAT) cohort. We compared the support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGB), and Wide and Deep (W&D) models. The area under the receiver operating curve (AUC) and accuracy (ACC) were used to evaluate the diagnostic efficiency of the models.
RESULTS:The WCHAT cohort, which included a total of 4057 participants for the training and testing datasets, and the XMAT cohort, which consisted of 553 participants for the external validation dataset, were enrolled in this study. Among the four models, W&D had the best performance (AUC = 0.916 ± 0.006, ACC = 0.882 ± 0.006), followed by SVM (AUC =0.907 ± 0.004, ACC = 0.877 ± 0.006), XGB (AUC = 0.877 ± 0.005, ACC = 0.868 ± 0.005), and RF (AUC = 0.843 ± 0.031, ACC = 0.836 ± 0.024) in the training dataset. Meanwhile, in the testing dataset, the diagnostic efficiency of the models from large to small was W&D (AUC = 0.881, ACC = 0.862), XGB (AUC = 0.858, ACC = 0.861), RF (AUC = 0.843, ACC = 0.836), and SVM (AUC = 0.829, ACC = 0.857). In the external validation dataset, the performance of W&D (AUC = 0.970, ACC = 0.911) was the best among the four models, followed by RF (AUC = 0.830, ACC = 0.769), SVM (AUC = 0.766, ACC = 0.738), and XGB (AUC = 0.722, ACC = 0.749).
CONCLUSIONS:The W&D model not only had excellent diagnostic performance for sarcopenia but also showed good economic efficiency and timeliness. It could be widely used in primary health care institutions or developing areas with an aging population.
TRIAL REGISTRATION:Chictr.org, ChiCTR 1800018895.
