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Vol.11, No.3, August 2022. ISSN: 2217-8309 eISSN: 2217-8333
TEM Journal
TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS Association for Information Communication Technology Education and Science |
Experimental Validation and Development of Decision Tree - based System for Prediction of Service Management of Perfusors / Syringe Pump
Becir Isakovic, Zerina Masetic, Jasmin Kevric, Lejla Gurbeta, Enis Gegic
© 2022 Becir Isakovic, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)
Citation Information: TEM Journal. Volume 11, Issue 3, Pages 1242-1253, ISSN 2217-8309, DOI: 10.18421/TEM113-33, August 2022.
Received: 06 June 2022. Revised: 04 August 2022.
Abstract:
Despite the fact that technology is improving day by day and that the medical devices (MDs) are being constantly upgraded, their malfunction is not a rare occurrence. The aim of this research is to develop an expert system that can predict whether the device will satisfy functional and safety requirements during a regular inspection. This expert system can be seen as part of Industry 4.0 that is revolutionizing medical device management. In order to develop the system, five machine learning algorithms that are representative of each classifier group, were used: (1) Random Forest, (2) Decision Tree, (3) Support Vector Machine, (4) Naive Bayes, (5) k-Nearest Neighbour. The Decision Tree outperformed other classifiers achieving the classification accuracy of 100% with and without attribute selection applied on the dataset. This study showed that machine learning algorithms can be used in order to predict MDs performance and potential failures in order to make the process of maintenance of medical devices more convenient and sophisticated and it is one step in modernizing medical device management systems by utilizing artificial intelligence.
Keywords – decision Tree, perfusor, medical device, maintenance, Industry 4.0., artificial intelligence. |
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