基于多导睡眠图的针刺失眠疗效预测OA

Insomnia is a chronic health condition that significantly impairs patients'quality of life and mental well-being.Although acupuncture is widely used in the treatment of insomnia due to its safety and efficacy,significant inter-individual variability in treatment response and the absence of a pre-treatment predictive mechanism limit the development of precise medication.To address this issue,a predictive framework integrating feature engineering and multi-model ensemble learning was constructed based on pre-treatment polysomnographic parameters.To handle the small sample size and class imbalance,adaptive synthetic sampling was applied to expand the training set,and recursive feature elimination with cross-validation was used to select the optimal feature subset.Five classification models—support vector machine,random forest,logistic regression,XGBoost,and artificial neural network—were compared using stratified 3-fold and leave-one-out cross-validation.Results indicated that support vector machine achieved an accuracy of 80.00%and a weighted F1-score of 83.75%under stratified cross-validation,but its performance declined in leave-one-out validation.In contrast,random forest and XGBoost demonstrated consistent stability and robustness across both validation methods,with random forest achieving the highest leave-one-out cross-validation accuracy of 90.00%and an F1-score of 85.26%.Shapley Additive Explanations analysis revealed that"sleep latency"and"deep sleep"were consistently identified as important features across multiple models,demonstrating strong physiological interpretability and generalizability.A simplified prediction rule based on these key features achieved a sensitivity of 83%and specificity of 71%in an independent test set,enabling effective identification of potential high-responders.A stratification strategy based on prediction results was further proposed,and prospective validation confirmed its efficacy in significantly improving the treatment response rate among initial low-responders.