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Deep learning used by the medical system would focus on predicting the specific diseases such as brain tumor, lung cancer, chronic kidney disease, and Parkinson's disease. To overcome the above said disadvantage the proposed work aims at a single system multiclass disease prediction using state, of, art deep learning models and web based prediction platform built with the help of Streamlit. The proposed work employs TensorFlow to train the deep learning models for specific diseases, the trained model parameters are stored with help of Python pickling and are reclaimed during prediction with the help of unpickling. Based on the selected disease the user enters either the clinical data or images which is passed through the optimized deep learning model and produces the prediction. The experimental result shows that the proposed framework is very effective and outperforms compared with the other deep learning methods with the accuracy of 99.89 % for Mask R, CNN when compared with 95.56 % and 92.47 % accuracy of FCN and LSTM respectively. It also provides integrated platform for early detection, timely treatment and effective decision making to relieve the burden on doctors. The framework proposed helps in predicting multiple disease and makes real, time prediction from a single platform.