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ORIGINAL RESEARCH article

Front. Artif. Intell.
Sec. Machine Learning and Artificial Intelligence
Volume 7 - 2024 | doi: 10.3389/frai.2024.1506770

Advanced Sleep Disorder Detection Using Multi-Layered Ensemble Learning and Advanced Data Balancing Techniques

Provisionally accepted
Muhammad Mostafa Monowar Muhammad Mostafa Monowar 1Sm Nuruzzaman Nobel Sm Nuruzzaman Nobel 2Maharin Afroj Maharin Afroj 2Md Abdul Hamid Md Abdul Hamid 1Md Zia Uddin Md Zia Uddin 3*Mohsin Kabir Mohsin Kabir 4M. F. Mridha M. F. Mridha 5*
  • 1 King Abdulaziz University, Jeddah, Makkah, Saudi Arabia
  • 2 Bangladesh University of Business and Technology, Dhaka, Dhaka, Bangladesh
  • 3 Sustainabale Communication Technologies, Oslo, Norway
  • 4 University of Girona, Girona, Catalonia, Spain
  • 5 American International University-Bangladesh, Dhaka, Bangladesh

The final, formatted version of the article will be published soon.

    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 8-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.

    Keywords: machine learning, sleep disorder, Ensemble approach, Explainable AI, healthcare, diagnosis, Ensemble models

    Received: 07 Oct 2024; Accepted: 30 Dec 2024.

    Copyright: © 2024 Mostafa Monowar, Nobel, Afroj, Hamid, Uddin, Kabir and Mridha. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Md Zia Uddin, Sustainabale Communication Technologies, Oslo, Norway
    M. F. Mridha, American International University-Bangladesh, Dhaka, Bangladesh

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.