Research on automatic diagnosis and analysis of medical equipment faults based on machine learning algorithms

With the wide application of medical equipment in the modern medical system, the stability and reliability of its operation directly affects the quality of medical services and the life safety of patients. Traditional medical equipment fault diagnosis mainly relies on manual detection and expert experience, which is not only inefficient, but also difficult to meet the increasingly complex operational requirements of modern medical equipment. In recent years, the rapid development of machine learning technology provides new solutions for medical equipment fault diagnosis. This paper discusses the automatic diagnosis method based on machine learning algorithms for the problem of medical equipment fault diagnosis, focusing on the analysis of data acquisition and pre-processing, model selection and optimization, and diagnostic performance evaluation and other key links. Combined with practical application cases, this paper verifies the effectiveness of machine learning algorithms in medical equipment fault detection, and further explores the future development direction of intelligent diagnosis system. The results show that the machine learning-based fault diagnosis method can effectively improve the accuracy and real-time diagnosis, which provides important support for the intelligent operation and maintenance of medical equipment.