Digital solutions have emerged in recent years that improve patient care and diagnostic efficiency as a result of the healthcare industry's integration of intelligent technologies and machine learning. Through the analysis of user-reported symptoms, this project presents an intelligent, web-based health assistant that employs machine learning techniques to identify possible diseases. The system provides individualized treatment recommendations, including prescription drugs, diets, physical activity, and appropriate safety measures, in addition to basic diagnostics. Incorporating Support Vector Machine (SVM) models that have been trained on structured datasets of symptoms and diseases guarantees real-time forecasts and recommendations that are based on confidence. Users, physicians, and administrators can access it based on their roles, and an interactive dashboard is available to track activities. The system's objectives are to lessen the diagnostic burden on healthcare facilities, empower proactive healthcare decisions, and improve accessibility.
Revankar et al. (Tue,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: