Construction of Neural Network Model for Exercise Load Monitoring and Rehabilitation Therapy under Intelligent Medical Information Fusion Algorithm

Machine learning has a wide range of applications in the medical field. Medical big data has great application prospects, and by analyzing massive medical data, errors can be effectively avoided. When making objective judgments, this article establishes an evaluation model, and on this basis, uses machine learning methods to construct a health management system based on data learning. The research focuses on medical big data and clinical data, studying their characteristics and regulatory methods, providing more space for medical equipment management, ward management, medication management, medical record archive management, and outpatient diagnostic information query. The final experiment proved that although the training time of the paper’s algorithm (2 hidden layers) is longer than others, the training accuracy is better. When the number of hidden nodes in the two hidden layers is (14, 5, 7), the training time, training accuracy, and detection accuracy are 1.21 seconds, 79.23%, and 76.8%, respectively. Overall, the performance of the neural network model in the two hidden layers is better than that in the one hidden layer.