Research Design Approaches in MediNet – AI Health Risk and Smart Hospital Finder

Abstract: The majority of people do not seek medical attention until the condition becomes critical. This is because the usual health-related applications do not have anything innovative to offer beyond 5 basic symptom checkers. Moreover, they do not take into consideration the lifestyle of the user, like sleep patterns, diet, level of exercise, and stress levels, which are the actual causes of health risks. In the case of MediNet, the application of symptoms and daily routines is used to identify the potential risks of health complications, allowing the user to take necessary precautions rather than waiting for the condition to be critical. Moreover, this application also offers a feature that can be referred to as Smart Hospital Finder, which can automatically locate the nearest hospital that suits the health state of the user and provide the most convenient route to 2 that hospital via a map. In order to enable the user to track their health time, this application offers a feature that can be referred to as a Centralized Dashboard. The system is built using React.js, Node.js/Flask backend, MySQL database, and a Random Forestbased prediction model for health risk prediction, demonstrating that intelligent digital systems can meaningfully improve early diagnosis and reduce health risks.

Keywords: Artificial Intelligence, Health Risk Prediction, Random Forest, Smart Hospital Finder, Dijkstra's Algorithm, Preventive Healthcare, Machine Learning, Web-based Healthcare Platform, Lifestyle Analysis, Symptom Checker