Risk Assess: A Symptom-Based Disease Predictor

Abstract

Abstract: As of late, the medical care area has seen extraordinary changes through the joining of state of data collection. This study presents "Risk Assess", an imaginative web application fastidiously created with Flask. “Risk Assess” fills in as a complete stage, working with patient vitals for generating risk assessments. This python-based web application uses different Machine Learning models such as Classification and Regression (CART), Linear Support Vector Machine (SVM), Gaussian Naïve Bayes (NB), K-Nearest Neighbor (KNN) to analyze and build a model to predict if the given set of symptoms leads to a particular disease. Outstandingly, “Risk Assess” soothes out Cancer, Diabetes, Heart –Disease, Kidney-Disease and LiverDisease based prediction upgrading comfort and functional productivity. Our undertaking highlights a solid login page empowering clients to make novel qualifications utilizing their email addresses. By requiring a username and secret key attached to their email, we focus on both security and client comfort. This research paper contains working engineering, plan standards, and execution complexities, featuring the pivotal job of Machine Learning. “Risk Assess” epitomizes utilization of Python-based advances and Machine Learning, exhibiting the potential for predictions based on symptoms through creative computerized arrangements