Title: Enhanced crow search algorithm for early detection of Parkinson's disease in physically challenged patients

Authors: Raliya Abubakar; Mohamed Hamada; Mohammed Hassan; Saratu Yusuf Ilu; Jamilu Usman Waziri

Addresses: Department of Computer Science, Bayero University, Kano, Nigeria ' University of Aizu, Japan ' Department of Software Engineering, Bayero University, Kano, Nigeria ' Department of Software Engineering, Bayero University, Kano, Nigeria ' Department of Mathematical Science, Bauchi State University Gadau, Bauchi State, Nigeria

Abstract: Diagnosing Parkinson's disease at its early stage is the central issue in the treatment of patients so they can live productive lives for as long as possible. The research to date have tended to concentrate only on predicting the disease rather than predicting it in the early stage. Furthermore, there is an increasing concern to extend the efficiency of the existing models to include physically challenged patients. In this paper, we proposed an enhanced version of the crow search algorithm (ECSA) to improve the diagnosis of Parkinson's disease. The proposed ECSA was used as an optimiser in predicting Parkinson's disease in physically challenged individuals like the blinds, lepers, and disabled. The search is centered towards helping the physically challenged individual to have proper treatment at an early stage. The experimental results gave as high prediction accuracy as 95%. The performance of the proposed enhanced algorithm was compared with that of the original CSA. The result of the experiment reveals that the proposed enhanced algorithm outperforms the original CSA.

Keywords: Parkinson's disease; evolutionary algorithms; crow search; machine learning; neurodegenerative disorder; disease detection; decision tree; random forest; KNN; logistic regression.

DOI: 10.1504/IJENTTM.2022.129629

International Journal of Entertainment Technology and Management, 2022 Vol.1 No.4, pp.273 - 289

Received: 24 May 2022
Received in revised form: 15 Sep 2022
Accepted: 19 Sep 2022

Published online: 17 Mar 2023 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article