Manufacturer-Oriented Risk Assessment of Imaging Systems with Multi-Criteria Decision Making and Machine Learning

Abstract

This study presents a hybrid Multi-Criteria Decision Making (MCDM) and Machine Learning (ML) framework designed to support operational decisions related to the upgrade or replacement of medical imaging systems. Focusing on Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) units installed across healthcare facilities in Egypt, the research offers a novel contribution by analyzing equipment lifecycle management from the manufacturer's perspective, rather than that of the healthcare provider. A real-world dataset from Siemens Healthineers was used, incorporating six performance indicators such as system age, utilization rate, and downtime. Two sets of weights were applied-objective CRiteria Importance Through Intercriteria Correlation (CRITIC) and expert-based (Service Marketing Head)-within the Evaluation based on Distance from Average Solution (EDAS) method to generate replacement prioritization rankings. Furthermore, four supervised ML algorithms were trained to classify systems into three lifecycle stages. This hybrid approach demonstrated a classification accuracy of up to 96.5 % in identifying these classes. Results highlight the value of integrating domain expertise and data-driven techniques for supporting lifecycle planning, installed base retention, and service strategy development in the medical imaging industry.