Title: Decision making support system for medical devices maintenance using artificial neuro fuzzy inference system

Authors: Akram AlSukker; Nour Afiouni; Morad Etier; Mohannad Jreissat

Addresses: Department of Industrial Engineering, Faculty of Engineering, The Hashemite University, Zarqa 13133, P.O. Box 330127, Hashemite Kingdom of Jordan ' Department of Industrial Engineering, Faculty of Engineering, The Hashemite University, Zarqa 13133, P.O. Box 330127, Hashemite Kingdom of Jordan ' Department of Industrial Engineering, Faculty of Engineering, The Hashemite University, Zarqa 13133, P.O. Box 330127, Hashemite Kingdom of Jordan ' Department of Industrial Engineering, Faculty of Engineering, The Hashemite University, Zarqa 13133, P.O. Box 330127, Hashemite Kingdom of Jordan

Abstract: Reliable and successful maintenance management system is needed to achieve the best system with lowest costs. The lack of proper medical devices maintenance management in healthcare facilities is leading to unreliable usage of medical devices. This study focused on the decision making process of maintenance of medical devices. Each device was classified according to multiple factors, such as their function, age, price, risk, availability, and utilisation. Artificial neuro fuzzy inference system (ANFIS) was used to choose the best maintenance strategy and compared to neural networks, fuzzy inference system (FIS), and linear regression. Results showed that the best applied method was ANFIS using subtractive clustering in terms of testing data accuracy, with the highest accuracy of 82.99% compared to neural networks (78.16%) and ordinal logistic regression (73.47%). This study recommends incorporating ANFIS approach to healthcare facilities medical devices maintenance management leading to better healthcare services with minimum costs.

Keywords: decision making; maintenance management; neural network; artificial neuro fuzzy inference system; ANFIS; medical devices.

DOI: 10.1504/IJISE.2023.135775

International Journal of Industrial and Systems Engineering, 2023 Vol.45 No.4, pp.484 - 500

Received: 13 Dec 2021
Accepted: 16 Mar 2022
Published online: 05 Jan 2024 *

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