Degree of Difference in Clinical Data and Imaging Based on Machine Learning and Complex Network

Clinical patients should have corresponding clinical indicators to characterize the disease. Multimodal data and physiological indicators provide a basis for patient diagnosis and assessment, but small sample data pose statistical difficulties. In order to better support the clinical conclusions, from a data-driven perspective, using machine learning algorithms, we explored the support of physiological indicator data for multimodal data in the case of insufficient samples, and according to the results of the model, it is shown that the data-driven results can better support the final conclusions, and therefore, integrating the multimodal data and the clinical indicators can better provide the diagnosis and assessment conclusions for the clinical patients.