Construction of an interpretable machine learning-based prediction model for the clinical effect on ischemic stroke in treatment with eye acupuncture combined with rehabilitation therapy.
10.13703/j.0255-2930.20241028-0001
- Author:
Zhan ZHANG
1
;
Delong JIANG
1
;
Qingyan WANG
1
;
Pengqin WANG
2
Author Information
1. College of TCM, Liaoning University of TCM, Shenyang 110847, China.
2. Second Department of Encephalopathy Rehabilitation, Affiliated Hospital of Liaoning University of TCM, Shenyang
- Publication Type:Journal Article
- Keywords:
clinical effect;
eye acupuncture rehabilitation therapy;
ischemic stroke;
machine learning;
prediction model
- MeSH:
Humans;
Acupuncture Therapy;
Machine Learning;
Male;
Female;
Middle Aged;
Ischemic Stroke/rehabilitation*;
Aged;
Stroke Rehabilitation;
Adult;
Eye
- From:
Chinese Acupuncture & Moxibustion
2025;45(5):559-567
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To construct a prediction model for the clinical effect of eye acupuncture combined with rehabilitation therapy on ischemic stroke based on interpretable machine learning.
METHODS:From January 1st, 2020 to October 1st, 2024, the clinical data of 470 patients with ischemic stroke were collected in the the Second Department of Encephalopathy Rehabilitation of the Affiliated Hospital of Liaoning University of TCM. The modified Barthel index (MBI) score before and after treatment was used to divide the patients into an effect group (291 cases) and a non-effect group (179 cases). Random forest and recursive feature elimination with cross-validation were combined to screen the predictors of the therapeutic effect of patients. Seven representative machine learning models with different principles were established according to the screening results. The predictive effect of the best model was evaluated by receiver operating characteristics (ROC), calibration, and clinical decision-making (DCA) curves. Finally, the Shapley additive explanation (SHAP) framework was used to interpret the prediction results of the best model.
RESULT:①All the machine learning models presented the area under curve (AUC) to be above 85%. Of these models, the random forest model showed the best prediction ability, with AUC of 0.96 and the precision of 0.87. ②The prediction probability of calibration curve and the actual probability showed a good prediction consistency. ③The net benefit rate of DCA curve in the range of 0.1 to 1.0 was higher than the risk threshold, indicating a good effect of model. ④SHAP explained the characteristic values of variables that affected the prediction effect of the model, meaning, more days of treatment, lower MBI score before treatment, lower level of fibrinogen, shorter days of onset and younger age. These values demonstrated the better effect of eye acupuncture rehabilitation therapy.
CONCLUSION:The rehabilitation effect prediction model constructed in this study presents a good performance, which is conductive to assisting doctors in formulating targeted personalized rehabilitation programs, and identifying the benefit groups of eye acupuncture combined with rehabilitation therapy and finding the advantageous groups with clinical effect. It provides more ideas for the treatment of ischemic stroke with eye acupuncture combined with rehabilitation therapy.
