Publication

Abstract : Public health threats and epidemics are affecting the human life day-to-day. These include obesity, diabetes, cardiovascular diseases, cancer, osteoporosis and dental diseases. The trouble of chronic diseases may be cured during initial stages if it is properly predicted and it requires a lot of training using the medical data. Existing models can support disease diagnosis to certain extent based on the training. This can predict unique diseases and a separate system is required for different diagnosis. A generic disease prediction model can reduce the burden of physicians while making clinical decisions and has not been evolved yet. Machine learning and artificial intelligence offer one such generic model with a principled approach for intelligent disease diagnosis. A novel hybrid algorithm and a prediction model are proposed based on disease symptoms collected from hospitals and stored in the cloud. The features of K-Nearest Neighbour (KNN) and Convolutional Neural Network (CNN) are combined to provide high-speed prediction analysis. The enormous amount of medical data helps the proposed system identify hidden patterns associated with individual diseases