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Different regions exhibit unique characteristics of certain regional diseases, which may weaken the prediction of disease outbreaks. With big data growth in biomedical and healthcare communities, accurate analysis of medical data benefits early disease detection, patient care, and community services. So machine learning algorithms for effective prediction of chronic disease outbreak in disease-frequent communities are adopted. But analysis accuracy is reduced when the quality of medical data is incomplete. To overcome the difficulty of incomplete data, data processing needs to be done. A new convolutional neural network (CNN) disease risk prediction algorithm using structured and unstructured data from hospital is implemented. Compared with several typical prediction algorithms, the proposed system produces high accuracy, high performance and high convergence speed. This is faster than that of the traditional machine learning algorithms.

Disease prediction in big data healthcare using extended convolutional neural network techniques