TY  - EJOU
AU  - Hamza, Manar Ahmed 
AU  - Negm, Noha 
AU  - Al-Otaibi, Shaha 
AU  - Alhussan, Amel A. 
AU  - Duhayyim, Mesfer Al 
AU  - Al-Yarimi, Fuad Ali Mohammed 
AU  - Rizwanullah, Mohammed 
AU  - Yaseen, Ishfaq 

TI  - Evolutionary Algorithsm with Machine Learning Based Epileptic Seizure Detection Model
T2  - Computers, Materials \& Continua

PY  - 2022
VL  - 72
IS  - 3
SN  - 1546-2226

AB  - Machine learning (ML) becomes a familiar topic among decision makers in several domains, particularly healthcare. Effective design of ML models assists to detect and classify the occurrence of diseases using healthcare data. Besides, the parameter tuning of the ML models is also essential to accomplish effective classification results. This article develops a novel red colobuses monkey optimization with kernel extreme learning machine (RCMO-KELM) technique for epileptic seizure detection and classification. The proposed RCMO-KELM technique initially extracts the chaotic, time, and frequency domain features in the actual EEG signals. In addition, the min-max normalization approach is employed for the pre-processing of the EEG signals. Moreover, KELM model is used for the detection and classification of epileptic seizures utilizing EEG signal. Furthermore, the RCMO technique was utilized for the optimal parameter tuning of the KELM technique in such a way that the overall detection outcomes can be considerably enhanced. The experimental result analysis of the RCMO-KELM technique has been examined using benchmark dataset and the results are inspected under several aspects. The comparative result analysis reported the better outcomes of the RCMO-KELM technique over the recent approaches with the  of 0.956.
KW  - Epileptic seizures; eeg signals; machine learning; kelm; parameter tuning; rcmo algorithm

DO  - 10.32604/cmc.2022.027048