Due to big data and progress in biomedical and healthcare communities, accurate study of medical data benefits early disease recognition, patient care and community services. When the quality of medical data is incomplete, the exactness of study is reduced. In the proposed system, our system can take either text or image input symptoms from the user and based on the analysis of the symptoms it displays a result. It provides machine learning algorithms for effective prediction of various disease occurrences in disease-frequent societies. It experiment the altered estimate models over real-life hospital data collected. To overcome the difficulty of incomplete data, it uses a latent factor model to rebuild the missing data. It experiments on various diseases that occur in human being. Using structured and unstructured data from hospital, Random Forest algorithm is used for classification of text datasets. SSD (Single Shot Multi Box Detector) algorithm is used for image processing to analyse various diseases in human being.

Big Data, Machine Learning, kaggle, CNN

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