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A Novel Scheme for Classification of Epilepsy Using Machine Learning and a Fuzzy Inference System Based on Wearable-Sensor Health Parameters

Author

Listed:
  • Ankush Kadu

    (School of Electrical and Electronics Engineering, Lovely Professional University, Punjab 144402, India)

  • Manwinder Singh

    (School of Electrical and Electronics Engineering, Lovely Professional University, Punjab 144402, India)

  • Kingsley Ogudo

    (Department of Electrical and Electronics Engineering Technology, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg 2092, South Africa)

Abstract

The tremendous growth of health-related digital information has transformed machine learning algorithms, allowing them to deliver more relevant information while remotely monitoring patients in modern telemedicine. However, patients with epilepsy are likely to die or have post-traumatic difficulties. As a result, early disease detection could be essential for a person’s survival. Hence, early diagnosis of epilepsy based on health parameters is needed. This paper presents a classification of epilepsy disease based on wearable-sensor health parameters that use a hybrid approach with ensemble machine learning and a fuzzy logic inference system. The ensemble machine learning classifiers are used to predict epilepsy events using ensemble bagging and ensemble boosting regression. The experimental results show that compared to the ensemble bagging classifiers and other state-of-the-art methods, the ensemble boosting classifier with the fuzzy inference system outperformed with a 97% accuracy rate.

Suggested Citation

  • Ankush Kadu & Manwinder Singh & Kingsley Ogudo, 2022. "A Novel Scheme for Classification of Epilepsy Using Machine Learning and a Fuzzy Inference System Based on Wearable-Sensor Health Parameters," Sustainability, MDPI, vol. 14(22), pages 1-20, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:15079-:d:972567
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    References listed on IDEAS

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    1. Khondker Mohammad Zobair & Louis Sanzogni & Luke Houghton & Md Zahidul Islam, 2021. "Forecasting care seekers satisfaction with telemedicine using machine learning and structural equation modeling," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-31, September.
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