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

Ankush Kadu, Manwinder Singh () and Kingsley Ogudo
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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

Sustainability, 2022, vol. 14, issue 22, 1-20

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.

Keywords: healthcare; machine learning; epilepsy; fuzzy logic inference system; telemedicine (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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