A Dual Pipeline Machine Learning Framework for Automated Multi Class Sleep Disorder Screening Using Hybrid Resampling and Ensemble Learning

TL;DR

This paper introduces a dual pipeline machine learning framework combining statistical and wrapper-based methods with hybrid resampling to accurately classify multiple sleep disorders efficiently, potentially aiding large-scale screening.

Contribution

The novel dual pipeline approach integrates linear and non-linear feature selection with hybrid resampling, improving sleep disorder classification accuracy over existing methods.

Findings

Achieved 98.67% accuracy with Extra Trees and KNN.

Significant improvement over recent baselines.

Inference latency below 400 milliseconds.

Abstract

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.