Advanced sleep disorder detection using multi-layered ensemble learning and advanced data balancing techniques.

Journal: Frontiers in artificial intelligence
Published Date: Jan 28, 2025

Abstract

Sleep disorder detection has greatly improved with the integration of machine learning, offering enhanced accuracy and effectiveness. However, the labor-intensive nature of diagnosis still presents challenges. To address these, we propose a novel coordination model aimed at improving detection accuracy and reliability through a multi-model ensemble approach. The proposed method employs a multi-layered ensemble model, starting with the careful selection of N models to capture essential features. Techniques such as thresholding, predictive scoring, and the conversion of Softmax labels into multidimensional feature vectors improve interpretability. Ensemble methods like voting and stacking are used to ensure collaborative decision-making across models. Both the original dataset and one modified using the Synthetic Minority Oversampling Technique (SMOTE) were evaluated to address data imbalance issues. The ensemble model demonstrated superior performance, achieving 96.88% accuracy on the SMOTE-implemented dataset and 95.75% accuracy on the original dataset. Moreover, an eight-fold cross-validation yielded an impressive 99.5% accuracy, indicating the reliability of the model in handling unbalanced data and ensuring precise detection of sleep disorders. Compared to individual models, the proposed ensemble method significantly outperformed traditional models. The combination of models not only enhanced accuracy but also improved the system's ability to handle unbalanced data, a common limitation in traditional methods. This study marks a significant advancement in sleep disorder detection through the integration of innovative ensemble techniques. The proposed approach, combining multiple models and advanced interpretability methods, promises improved patient outcomes and greater diagnostic accuracy, paving the way for future applications in medical diagnostics.

Authors

  • Muhammad Mostafa Monowar
    Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
  • S M Nuruzzaman Nobel
    Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh.
  • Maharin Afroj
    Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh.
  • Md Abdul Hamid
    Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
  • Md Zia Uddin
    Sustainable Communication Technologies, SINTEF Digital, Oslo, Norway.
  • Md Mohsin Kabir
    Superior, Polytechnic School, University of Girona, Girona, Spain.
  • M F Mridha
    Department of Computer Science and Engineering, American International University, Dhaka, Bangladesh.

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