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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences
Fabian Halley Pata Alban Dattu
i-CATS University College
Computer Science & IT Earth & Planetary Sciences Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Hybrid machine learning for predictive spare parts replacement in medical equipment Fabian Halley Pata Alban Dattu; Syed Tarmizi Syed Shazali; Shirley Jonathan Tanjong; Abdul Rani Achmed Abdullah; Nurlaila Rosli
International Journal of Advances in Applied Sciences Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i3.pp990-997

Abstract

Stability of medical equipment is critical for uninterrupted healthcare services, yet hospital computerized maintenance management systems (CMMS) records are often incomplete, heterogeneous, and partially labeled for spare parts usage. In the analyzed CMMS dataset, spare-part-related work orders accounted for approximately 65.1% of maintenance cases, indicating that spare-part readiness is a major contributor to maintenance response time and potential equipment downtime. This study proposes a semi‑supervised hybrid random forest-neural network (RF-NN) model to predict whether a work order will require spare‑parts replacement and to support proactive inventory planning. Retrospective CMMS work‑order records from six hospitals (N = 295) were pre‑processed using leakage‑safe pipelines (categorical one‑hot encoding, text feature extraction from fault descriptions, and standardization of numeric fields). Semi‑supervised self‑training was used to exploit unlabeled or weakly labeled records through iterative pseudo‑labelling. Models were evaluated using stratified 10‑fold cross‑validation and reported as mean ± SD. The hybrid RF-NN achieved Accuracy = 0.986 ± 0.018, precision = 0.980 ± 0.026, recall = 1.000 ± 0.000, and F1‑score = 0.990 ± 0.013, outperforming RF, NN, and Markov baselines. These results demonstrate the practical advantage of combining semi‑supervised learning with hybrid modelling to reduce missed spare‑parts cases, minimize downtime risk, and strengthen CMMS‑ready procurement decision support.
Machine learning and failure mode and effects analysis for sustainable medical equipment spare parts management Fabian Halley Pata Alban Dattu; Syed Tramizi Syed Shazali; Shirley Johnathan Tanjong; Abdul Rani Achmed Abdullah; Nurlaila Rosli
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4273-4282

Abstract

Spare parts for essential medical equipment are vital to ensure uninterrupted healthcare provision, especially in areas where resources are limited. This paper presents an integrative approach that integrates failure mode and effects analysis (FMEA) with supervised machine learning (ML) models in order to allow better predictive maintenance and optimize management of spare parts. In order to increase the accuracy of the forecast, FMEA was first used to compute risk priority numbers (RPNs) of the components, quantifying the risk of each component, and then using it as an important input feature to the ML algorithms. Data from six hospitals and ten critical medical devices, such as ventilators, defibrillators, dialysis machines, and computed tomography (CT) scanners, were analyzed to identify high-risk failure modes and prioritize maintenance tasks. Three different supervised learning models (random forest (RF), artificial neural network (ANN), and support vector machine (SVM)) was built and tested based on four metrics: accuracy, precision, recall, and F1-score. The precision of RF and ANN was 1.00 and SVM showed an excellent recall value of 0.94, indicating promising results for practical applications. The solution proposed consists of a hierarchical decision system, connecting risk assessment carried out by experts with data-driven forecasts, to create proactive and sustainable management of spare parts. This approach helps healthcare entities lessen the risk of equipment breakdowns, adhere to international healthcare standards, and increase system reliability and efficiency.
Co-Authors Abdul Rani Achmed Abdullah Abdul Rani Achmed Abdullah Nurlaila Rosli Shirley Johnathan Tanjong Shirley Jonathan Tanjong Syed Tarmizi Syed Shazali Syed Tramizi Syed Shazali