With massive information development in medical specialty and aid community, precise analysis of medical information advantages premature disease detection, patient care and community services. although, the analysis accuracy is reduced once the standard of medical information is incomplete. moreover, completely different regions exhibit distinctive characteristics of bound regional diseases, which can weaken the prediction of illness outbreaks. during this paper, we tend to contour machine learning algorithms for effective prediction of chronic malady eruption in disease-frequent communities. we tend to experiment the tailored prediction models over real-life hospital information collected from central China in 2013-2015. to beat the problem of incomplete information, we tend to use a latent issue model to build the missing information. we tend to experiment on a regional chronic illness of cerebral infarction. we tend to propose a replacement convolutional neural network based multimodal disease risk prediction (CNN-MDRP) algorithmic program victimisation structured and unstructured information from hospital. To the simplest of our data, none of the prevailing work targeted on each information varieties within the space of medical massive information analytics. Compared to many typical prediction algorithms, the prediction accuracy of our projected algorithmic program reaches ninety four.8% with a convergence speed that is faster than that of the CNN-based unimodal disease risk prediction (CNN-UDRP) algorithmic program
