Development of an interpretable machine-learning model to predict insufficient functional improvement after aquatic therapy in stroke

Background Early identification of stroke survivors who are unlikely to achieve clinically meaningful motor improvement after aquatic therapy may help optimize rehabilitation prescriptions and support precision rehabilitation. This study aimed to develop and validate machine-learning models based on pretreatment Fugl-Meyer Assessment (FMA) data to predict insufficient motor improvement after aquatic therapy, to examine whether actual aquatic-therapy exposure improves prediction, and to identify the most important predictors. Methods We conducted a single-center retrospective study of patients with stroke who received the routine aquatic rehabilitation programme in the Department of Rehabilitation Medicine between January 2022 and December 2025. The primary outcome was insufficient improvement in total Fugl-Meyer motor function at day 56 ± 7 days after the start of aquatic therapy, defined as Δ FMA <10 points; total FMA was recalculated as FMA upper-extremity plus lower-extremity scores. Temporal validation was used: cases from January 2022 to December 2024 formed the training set, and cases from January to December 2025 formed the temporal validation set. Two modeling strategies were developed. Model A included baseline clinical information and the initial rehabilitation and aquatic-therapy plans available after prescription and before the first aquatic session. Model B added actual treatment-execution parameters accumulated through day 14 (allowable landmark window, days 12–16). ExtraTrees, Random Forest, XGBoost, logistic regression and K-nearest neighbors were compared, and model performance was assessed with the area under the receiver operating characteristic curve (AUC), calibration, decision-curve analysis (DCA) and Shapley additive explanations (SHAP). Results A total of 1,320 patients were included, of whom 409 had insufficient FMA improvement after aquatic therapy. The development set included 786 patients and 241 events, and the temporal validation set included 534 patients and 168 events. In the temporal validation set, Random Forest performed best for Model A, with an AUC of 0.818 [95% confidence interval (CI) 0.777–0.853] and Brier score of 0.172 (95% CI 0.160–0.186). At the frozen sensitivity-prioritized threshold of 0.408, Model A had an accuracy of 0.700, sensitivity of 0.857, specificity of 0.628, positive predictive value of 0.514, negative predictive value of 0.906 and F1 score of 0.643. Random Forest also performed best for Model B, with an AUC of 0.814 (95% CI 0.774–0.850) and Brier score of 0.174 (95% CI 0.163–0.186). The paired change in AUC was −0.004 (95% CI −0.022 to 0.014). Model A SHAP analysis highlighted planned aquatic-course duration, planned weekly aquatic frequency, baseline FMA-LE, baseline upper-extremity FMA and time from stroke onset to rehabilitation. Conclusions The interpretable Model A used routine clinical data and initial rehabilitation prescriptions available before the first aquatic session and was evaluated in a later cohort from the same center. Model B analysis illustrates how risk could be updated at day 14 once genuine execution records become available. These analyses do not show that model-guided treatment adjustment improves motor recovery; external validation and prospective clinical-impact evaluation are required before clinical use.