Health Monitoring System Using Machine Learning Techniques Algorithm

Abstract

Abstract: One of the most crucial aspects of someone's capacity to progress in life is their physical and mental well-being. Given the resources and requirements of society, the health-care system seeks to improve the populace as effectively as feasible. Due to a lack of timely medical equipment and treatments, death rates are growing in most nations. These health concerns can be prevented by offering standard healthcare services. The Flask framework was used to create the web application that houses our health monitoring system. In this Health Monitoring System, we employed Decision Tree Classification (Supervised Machine Learning method) to precisely anticipate outcomes. We used our own dataset to train and test our model. We could anticipate the patient's health level and area of risk based on that evaluation