To improve the prediction performance of patients with mild cognitive impairment(MCI)to the stage of Alzheimer's disease(AD),a semi-supervised neural network model MVIDG was proposed that integrated multiple examination data of pa-tients for learning.The dimensionality reduction of high-dimensional features was carried out through the mRMR algorithm,and the basic model training was performed on the patient's single inspection data using Dual-GCN,and the improved MVCDN net-work was used to fuse the models trained by each inspection data,to predict the patient's disease progression from MCI stage to AD stage in the next year.Experimental results show that the proposed model can effectively integrate multiple patient examina-tion results to improve prediction performance,and the effect is better than that of other data fusion methods.
维普
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