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
ZHANG Zhan, JIANG Delong, WANG Qingyan, et al. 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[J]. Chinese Acupuncture & Moxibustion, 2025, 45(5): 559-567.
ZHANG Zhan, JIANG Delong, WANG Qingyan, et al. 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[J]. Chinese Acupuncture & Moxibustion, 2025, 45(5): 559-567.DOI: 10.13703/j.0255-2930.20241028-0001.
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
2
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
2
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
2
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.