Abd Rahman, Noorul Husna and Hasikin, Khairunnisa and Abd Razak, Nasrul Anuar and Al-Ani, Ayman Khallel and Anni, D. Jerline Sheebha and Mohandas, Prabu (2023) Medical Device Failure Predictions Through AI-Driven Analysis of Multimodal Maintenance Records. IEEE Access, 11. pp. 93160-93179. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2023.3309671.
Full text not available from this repository.Abstract
Medical device failure and maintenance records are essential information, as some nations lack dedicated systems for capturing this valuable data. In addition to making healthcare more intelligent and individualized, machine learning has the potential to transform the conventional healthcare system. Optimizing AI models in decision-making could mitigate the effects of three research issues: malfunctioning medical devices, high maintenance costs, and the lack of a strategic maintenance framework. This study proposes a data-driven machine-learning model for predicting medical device failure. The proposed predictive model is developed using multimodal data of structured maintenance and unstructured text narrative of maintenance reports to predict the failure of 8,294 critical medical devices. In developing the model, 44 varieties of essential medical devices from 15 healthcare institutions in Malaysia are utilized. A classification problem is addressed by classifying failure into three prediction classes: (i) class 1, unlikely to fail within the first three years, (ii) class 2, likely to fail within three years; and (iii) class 3, likely to fail after three years from the date of commissioning. The topic modelling and synthesis strategy: Latent Dirichlet Allocation is applied to unstructured data in order to uncover concealed patterns in maintenance notes captured during failures. In addition, sensitivity analysis is performed to select only the most significant parameters affecting the failure performance of the medical device. Then, four machine learning algorithms and three deep learning networks are evaluated to determine the best predictive model. Based on the performance evaluation, the Ensemble Classifier is further optimized and demonstrates improved accuracy of 88.80%, specificity of 94.41%, recall of 88.82%, precision of 88.46%, and F1 Score of 88.84%. The study proves a reduction in intervention from 18 to 8 features and a reduction in training time from 1660.5 to 901.66 seconds for comprehensive model development.
| Item Type: | Article |
|---|---|
| Funders: | Ministry of Higher Education through MRUN Young Researchers Grant Scheme (MY-RGS) under Grant MR001-2019, Universiti Malaya Living Laboratory under Grant LL2022CN002, Universiti Malaya Grant under Grant RMF0401-2021 |
| Uncontrolled Keywords: | Artificial intelligence; machine learning; medical device failure prediction; medical device maintenance; maintenance cost |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering Faculty of Engineering > Department of Biomedical Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 30 Oct 2025 08:31 |
| Last Modified: | 30 Oct 2025 08:31 |
| URI: | http://eprints.um.edu.my/id/eprint/49957 |
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