Disease Prediction for the Deprived using Machine Learning

Abstract

Our work aims for economical disease diagnostics, by asking the user for Prognosis and symptoms, accurate disease prediction has been strived for. In aspiration for social welfare, the cost of using the product built is almost free, the prediction can be done using any one of the six algorithms, five out of which are total free of cost for use, those five being KNN, Naïve Bayes, SVM , Logistic Regression, K Means Classifier. The one, that gives out predictions with most accuracy, i.e., Decision Trees Classifier, has been made paid, others are not to be paid for, for using.How this product would be functioning is simple: User logs in , openCV has been used for it, that brings the user to the section where user is briefed about models working on different algorithms, each algorithm having different accuracy, thus further, which model he/ she should choose. On choosing model of their choice, they fill their symptoms and prognosis, that yields them their final result of name of their disease.Services like these are greatly needed , looking at large many number of people in our society, who are unfortunately not able to afford them, when priced heavily, or even moderately. Such products can help save many a lives, notify sufferer about his chronic disease at early stage, inform about deficiency diseases, that are very controllable, if get known about, early.