双流机器学习框架实现高效多类睡眠障碍自动筛查
A Dual Pipeline Machine Learning Framework for Automated Multi Class Sleep Disorder Screening Using Hybrid Resampling and Ensemble Learning
- 双管道设计：一管道用互信息与线性判别分析，另一管道用自编码器学非线性特征
- 在睡眠健康与生活方式数据集上达98.67%准确率，显著优于基线
- 适合需要快速、无创筛查的临床或公共卫生场景
准确分类失眠和睡眠呼吸暂停等睡眠障碍对降低长期健康风险、提升患者生活质量至关重要。然而，临床睡眠研究资源消耗大，难以大规模推广。本文提出一种双管道机器学习框架，基于睡眠健康与生活方式数据集实现多类睡眠障碍筛查。框架包含两个并行处理流：统计流利用互信息与线性判别分析增强线性可分性；包装流结合Boruta特征选择与自编码器进行非线性表征学习。为解决类别不平衡问题，采用SMOTETomek混合重采样策略。实验表明，Extra Trees与K近邻模型达到98.67%准确率，显著优于近期基线。威尔科克斯符号秩检验确认性能提升具有统计显著性，推理延迟低于400毫秒。结果表明，该双管道设计支持精准高效的自动化筛查，适用于无创睡眠障碍风险分层。
原文摘要 · Abstract (English)
Accurate classification of sleep disorders, particularly insomnia and sleep apnea, is important for reducing long term health risks and improving patient quality of life. However, clinical sleep studies are resource intensive and are difficult to scale for population level screening. This paper presents a Dual Pipeline Machine Learning Framework for multi class sleep disorder screening using the Sleep Health and Lifestyle dataset. The framework consists of two parallel processing streams: a statistical pipeline that targets linear separability using Mutual Information and Linear Discriminant Analysis, and a wrapper based pipeline that applies Boruta feature selection with an autoencoder for non linear representation learning. To address class imbalance, we use the hybrid SMOTETomek resampling strategy. In experiments, Extra Trees and K Nearest Neighbors achieved an accuracy of 98.67%, outperforming recent baselines on the same dataset. Statistical testing using the Wilcoxon Signed Rank Test indicates that the improvement over baseline configurations is significant, and inference latency remains below 400 milliseconds. These results suggest that the proposed dual pipeline design supports accurate and efficient automated screening for non invasive sleep disorder risk stratification.
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