PRESCRIPTO: A Modern Approach to HealthCare Scheduling

Abstract - In recent years, the demand for accessible and intelligent healthcare solutions has grown significantly, especially with the rise of digital health platforms. This paper presents Prescripto, a smart doctor appointment booking system enhanced with machine learning capabilities to provide preliminary disease prediction based on user-reported symptoms. Developed using the MERN (MongoDB, Express.js, React.js, Node.js) stack, the system offers a seamless interface for patients to schedule appointments with verified doctors. To further assist users in understanding their health conditions before consultation, a machine learning model—trained on a publicly available disease-symptom dataset—was integrated to predict probable diseases by analyzing user-input symptoms. The model leverages supervised learning algorithms such as Random Forest and Naive Bayes to ensure reliable and interpretable predictions. This integration not only improves user engagement but also aids in early detection and appropriate doctor selection. The paper discusses the architecture of the system, the implementation of the ML model, and the outcomes of model evaluation. Results demonstrate that the intelligent augmentation of healthcare platforms with ML enhances their practical utility, making Prescripto a scalable and effective solution for modern healthcare needs. Key Words: Doctor Appointment System, Digital Prescription, Healthcare Management, Scalable Web Application, Real-time Scheduling, Role-Based Access Control, Medical Software, Secure Patient Data, Healthcare.