Multi disease Prediction using CNN and Logistic Regression

Abstract

Preventable health problems can be avoided with the use of early detection methods, which are essential to improving the quality of treatment available to the public. Disease prediction and treatment response prediction are two areas where deep learning has shown to be particularly useful. Diseases including cancer, diabetes, and cardiovascular disorders have all seen increases in the Input, which is received through numerical data because to the rise of deep learning techniques and their impact on healthcare generally. An instantaneous, real-time output of a disease's likeliness, its likely causes, and its potential effects is obtained. The purpose of this study is to use real-time data, in the form of electronic health records, to forecast cancer and diabetes (EHR). The goal here is to use data from the CT scan and other chest x-rays to make an instantaneous diagnosis of pnuemonia. The top-tier application of machine learning and Deep Learning techniques. The goal is to develop an application-based server using the flask web framework.