集成技术2026，Vol.15Issue(3)：112-125,14.DOI:10.12146/j.issn.2095-3135.20251023001
基于多导睡眠图的针刺失眠疗效预测
Polysomnography-Based Prediction of Acupuncture Efficacy in Insomnia
摘要
Abstract
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.关键词
机器学习/特征选择/集成学习/交叉验证/沙普利加和解释Key words
machine learning/feature selection/ensemble learning/cross-validation/Shapley Additive Explanations分类
信息技术与安全科学引用本文复制引用
刘会芬,李奕萱,郭誉,丘江宁,林宇芬,张雯婕,樊小毛,曹雪梅..基于多导睡眠图的针刺失眠疗效预测[J].集成技术,2026,15(3):112-125,14.基金项目
深圳市科创委科研基金面上项目(JCYJ20220531092015036) （JCYJ20220531092015036）
广东省针灸学会科研基金项目(GDZJ2022004) （GDZJ2022004）
国家自然科学基金项目(62473267) This work is supported by General Program of the Shenzhen Science and Technology Innovation Commission(JCYJ20220531092015036),Research Fund Project of the Guangdong Provincial Acupuncture Society(GDZJ2022004)and National Natural Science Foundation of China(62473267) （62473267）
