Machine learning for improved medical device management: A focus on dialysis machines

Background Dialysis is a very complex treatment that is received by around 3 million people annually. Around 10% of the death cases in the presence of the dialysis machine were due to the technical errors of dialysis devices. One of the ways to maintain dialysis devices is by using machine learning and predictive maintenance in order to reduce the risk of patient's death, costs of repairs and provide a higher quality treatment. Objective Prediction of dialysis machine performance status and errors using regression models. Method The methodology includes seven steps: data collection, processing, model selection, training, evaluation, fine-tuning, and prediction. After preprocessing 1034 measurements, twelve machine learning models were trained to predict dialysis machine performance, and temperature and conductivity error values. Results Each model was trained 100 times on different splits of the dataset (80% training, 10% testing, 10% evaluation). Logistic regression achieved the highest accuracy in predicting dialysis machine performance. For temperature predictions, Lasso regression had the lowest MSE on training data (0.0058), while Linear regression showed the highest R² (0.59). For conductivity predictions, Lasso regression provided the lowest MSE (0.134), with Decision tree achieving the highest R² (0.2036). SVM attained the lowest MSE on testing dataset, with 0.0055 for temperature and 0.1369 for conductivity. Conclusion The results of this study demonstrate that clinical engineering (CE) and health technology management (HTM) departments in healthcare institutions can benefit from proposed automated systems for advanced management of dialysis machines.