Impact of machine learning on spare parts availability for critical medical devices: a supervised machine learning perspective

Purpose This paper seeks to improve the reliability and quality of operation of the critical medical equipment methods through the combination of failure mode and effects analysis (FMEA) and supervised machine learning (ML) approaches to predictive spare parts management. The paper aims to reduce downtime, optimise the maintenance planning and increase the quality of healthcare services in general by advanced decision support. Design/methodology/approach A dataset comprising 2,800 maintenance records from six hospitals, covering 10 categories of medical devices including ventilators, dialysis machines, infusion pumps and computed tomography scanners, was analysed. The FMEA was initially used to calculate risk priority numbers (RPNs) to indicate the criticality of devices and their probability of failure. These RPNs were used as input features to three supervised ML models, which are the random forest (RF), the artificial neural network (ANN) and the support vector machine (SVM). Each model underwent grid-search hyperparameter tuning and five-fold stratified cross-v