APPLICATION OF MACHINE LEARNING IN DISEASE PREDICTION

Abstract

As we know that technology is playing vital role in the medical sciences also. We are also
working on knowledge rather than the information so on the basis of our knowledge system that
we are already using in some areas like weather forecasting, emotions analysis etc. Similarly this
technique can also work in the disease forecasting, like Type 1, Type 2 diabetes, heart disease
forecasting. If we can forecast any disease that will be a great help for the humanities so with this
objective this study has been done. There are lot of other examples in which forecasting have
been done but there are still improvement is required in their accuracy rate, so this study also
tells that most of the previous studied have worked on structured and un-structured data but
existing study have more focused on structured data so accuracy rate should might be improved.
The analysis accuracy is increased by using Machine Learning algorithm and Map Reduce
algorithm. In this study we have also compared some of the algorithms based on estimation, as
per the calculationthe accuracy of the proposed algorithm is about 94.8% with a convergence
speed which is faster than that of the CNN-based unimodal disease risk prediction (CNN-UDRP)
algorithm.