Web-Based Health Monitoring System using Machine Learning Algorithms

Abstract

The current world is seeing the rise of the significance of offering quick and quality medical service, particularly amongst individuals residing in distant or hectic regions. The traditional systems are time consuming since they involve physical visits and manual processing which may slow down diagnosis and treatment. As a solution to this, a smart health monitoring system has been implemented which trains the machine learning algorithm to predict the disease based on symptoms that the user reports. The system will be a web-based program where patients can enter their symptoms and machine learning algorithms such as the Random Forest, Support Vector Machine (SVM), Logistic Regression, and Voting Classifier would analyse this information by revealing potential health problems. After the prediction, the process becomes more convenient, less time consuming since patients are able to schedule appointments with doctors directly using the same portal. The system was tested and evaluated to indicate a prediction accuracy of between 88% and 95%owing to the model used. Such accuracy assists in making more and faster decisions in the medical environment. In general, the system is efficient, convenient to use, and scalable since it cuts the amount of manual work and enhances the level of communication between patients and doctors, thereby making healthcare much more accessible and facilitated.