HEALIQ: A WEB-BASED DOCTOR APPOINTMENT AND DISEASE PREDICTION SYSTEM FOR SMART HEALTHCARE

Abstract

The growing demand for accessible and efficient healthcare has exposed the limitations of conventional appointment scheduling and disease-screening workflows. Manual systems often result in patient delays, scheduling conflicts, and miscommunication between medical professionals and their clients. Simultaneously, early disease detection remains out of reach for many due to limited diagnostic availability. This study presents HealiQ (Health IQ), a comprehensive full-stack web platform integrating digital appointment management with machine-learning-based disease prediction. The system combines a React–Node.js–MongoDB stack with a Flask- based ML module, enabling seamless interaction between patient and doctor interfaces and predictive analytics. The appointment workflow includes QR-code verification and email notifications to eliminate duplication and ensure visit confirmation, while ML models (Logistic Regression, AdaBoost, Gradient Boosting) provide risk estimation for stroke, diabetes, and general symptoms. Evaluation demonstrates improved workflow efficiency, reduced scheduling errors, and real-time health awareness. By bridging operational automation with intelligent diagnostics, HealiQ illustrates a scalable approach toward smart healthcare delivery.