Big data and its techniques not only help the biomedical and healthcare sectors to forecast the disease prediction but also the patients. It is difficult to meet the doctor at all the times in hospital for minor symptoms. Big data gives necessary information about the diseases based on the symptoms of the patient. Nowadays people wants to know more about their health, diseases and the related treatments for their betterment. However existing health care system gives structured input which lacks in reliable and accurate prediction. Here, Automatic Multi-Disease Prediction (AMDP) technique is proposed which identifies the most accurate disease based on patient’s input which benefits in early detection. Electronic Health Record (EHR) maintains and updates patient health records which facilitate an improved prediction model. Big data uses both structured and unstructured inputs which result in instant guidance to their health issues. The system takes input from the users which checks for various diseases associated with the symptoms based upon analyzing a variety of datasets. If the system is not able to provide suitable results, it intimate the users to go for Clinical Lab Test (CLT) such as blood test, x-ray, and scan so on where the uploaded images are sent for the effective deep learning prediction. The different parameters included in effective automatic multi disease prediction include preprocessing, clustering and predictive analysis. The main objective of the proposed system is to identify the diseases based on the symptoms and give proper guidance for the patients to take treatment quickly without making any further delay in a convenient and efficient manner.

Big Data, AMDP, EHR, Deep Learning Algorithm, CLT

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