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, Kingsley A. Ogudo
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.