Predicting Renal Disease and enhancing health through a Routine Tracker

Since the advent of COVID-19, people are attuned to a sedentary lifestyle. Medical reports suggest that a sedentary lifestyle increases the risk of diabetes which invariably affects the kidneys. The project is primarily focussed on predicting the status of the kidney disease using Machine Learning tools based on various factors ranging from age, gender to blood-pressure, cholesterol levels and scan reports. In order to ameliorate this condition which prevails among people, a Personal Routine Tracker application is developed which ensures them to track their daily activities and keep their records in check and visit them when required. It is a holistic approach as the application is built on an interface that facilitates ease of use and access by any layman. The ML model developed is evaluated by cross-validation for its accuracy [1].