Diverse Disease Prognostication through Machine Learning Models

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Today's generation faces various diseases due to the current atmosphere, pollution, poor quality of food, and their living habits. It is difficult for doctors to predict and analyze all the diseases manually; in most cases, the prediction of diseases goes wrong due to the large number of samples. The work aims to build a healthcare web application for identifying and predicting multiple diseases like heart disease, diabetes prediction, liver disease, breast cancer, kidney disease, etc., and machine learning models such as Decision trees, SVM, KNN, Random Forest, etc., used to accomplish this. To improve the accuracy level, datasets were gathered for every condition and trained them. we created an end-to-end web application using Flask framework where the user enters data to view the outcomes of various diseases' predictions. The drawbacks of the existing system are the users have to go to different sites to get different disease predictions, it becomes difficult for the user to move from one site to another, and in many cases, there is no proper user-friendly web application for disease prediction with no proper accuracy level mentioned. The proposed system focuses on developing a web application that offers users a variety of disease predictions based on their preferences. Multiple models were taken into consideration for training and testing the data. The evaluation results of each model were collected and then compared using a box plot