Disease Prediction System using Machine Learning

People are more concerned with their own well-being in our public at large. Custom well-being management is increasing step by step.Due to the absence of experienced doctors and specialists, the health demand of the public cannot be met to most social insurance associations. The public also need a gradually correct result. In line with this, a growing number of applications in information mining are being developed to give more altered medical services to individuals. A decent response to the confusion of the lack of therapeutic resources and the increasing demands for rehabilitation. To discover the link between usual physical test records and the potential well-being risk presented by or open to the customer, we propose a framework that uses information mining technologies. The main concept is to determine health problems according to the side effects, the daily routine of the user, together with the ability to provide the nearest doctor's office in this area. The framework offers examiners and specialists an easy-to-understand interface. Examiners can determine the different side effects in your body while experts can obtain many examinations with a potential risk. A critical component could spare work and therefore improve the implementation of the framework. The specialist can determine the results of forecasts via an interface, which gathers latest information from specialists. Allthis information will be regularly enabled by an additional preparation procedure. Our framework could thus naturally improve the execution of the forecast