Interdisciplinary Research for Predictive Maintenance of MRI Machines Using Machine Learning

Swapnali N Jagtap; Saiprasad Potharaju; Shanmuk Srinivas Amiripalli; Ravi Kumar Tirandasu; B J Jaidhan

doi:10.59796/jcst.V15N1.2025.78

Authors

Swapnali N Jagtap Department of Information Technology, K. K.Wagh Institute of Engineering Education & Research, Nashik, MH, India
Saiprasad Potharaju Department of CSE, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India
Shanmuk Srinivas Amiripalli Department of CSE, GST, GITAM University, Visakhapatnam, AP-530045, India
Ravi Kumar Tirandasu Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India
B J Jaidhan Department of CSE, GST, GITAM University, Visakhapatnam, AP-530045, India

DOI:

https://doi.org/10.59796/jcst.V15N1.2025.78

Keywords:

Healthcare, Machine Learning, MRI Machines, Predictive Maintenance, Reliability

Abstract

Predictive maintenance is crucial for ensuring the reliability and availability of medical equipment, particularly MRI machines in healthcare facilities. This study presents a comprehensive approach to predictive maintenance of MRI machines using machine learning techniques. The objective of this research is to develop and evaluate predictive models capable of identifying patterns and indicators of impending equipment failures, thereby improving the operational efficiency and reliability of MRI machines. We utilized a dataset comprising historical maintenance records, sensor readings, and environmental conditions collected from three 1.5 T Siemens MRI machines at MGM Hospital, Warangal, Telangana, India. The dataset, initially consisting of 96 records and expanded to 1000 through computer-generated data, encompasses various operational aspects, including temperature, humidity, vibration, power consumption, and coolant flow rate. This study investigated the efficacy of multiple machine learning algorithms for predicting equipment failures, including Random Forest, Gradient Boosting, Long Short-Term Memory (LSTM) networks, and Support Vector Machines (SVM). Model performance was evaluated using standard metrics such as F1-score, accuracy, recall, and precision. Results indicate that LSTM networks achieved the highest accuracy at 89%, while SVM displayed the lowest at 82%. These findings validate the potential of machine learning in anticipating equipment breakdowns and enabling proactive maintenance strategies for MRI machines. The outcomes of this research have significant implications for enhancing the reliability and operational efficiency of medical imaging equipment in healthcare settings.

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