Construction of a Prediction Model for Rehabilitation Training Effect Based on Machine Learning
Raymond Hu, Xiangzhou Jian, Jun Wang, Huayu Zhao · Preprints.org · 2025
Accurate prediction of rehabilitation training effect is important for the development of personalized rehabilitation program. In order to improve the prediction accuracy, a multimodal data fusion model based on machine learning is constructed to integrate clinical data, image data and physiological signals for feature extraction and optimization. Supervised learning methods are used for classification prediction, and unsupervised learning is combined for data pattern recognition and dimensionality reduction analysis. The experimental results show that the CNN performs best in several indicators and has strong generalization ability. The research results can provide data-driven decision support for rehabilitation training, improve the objectivity and accuracy of rehabilitation assessment, and promote the development of intelligent rehabilitation medicine.