Online Doctor Appointment using Machine Learning
In today's fast-paced world, accessing timely medical care can be challenging due to long wait times, scheduling difficulties, and a lack of proper guidance. The Doctor Appointment System addresses these issues by integrating AI and machine learning to create a more efficient and accessible healthcare experience. It ensures secure access with OTP-based authentication, preventing unauthorized logins. A machine learning-powered disease prediction model helps patients analyze symptoms and gain preliminary insights before consulting a doctor, while an AI-driven chatbot provides instant assistance, answering queries and guiding users through the appointment process. To enhance convenience, the system includes a Nearby Hospital Locator, allowing users to search for hospitals based on their city. An admin-controlled scheduling system manages appointments efficiently, enabling administrators to approve or reject requests based on doctor availability while maintaining a specialist patient limit to avoid overbooking. Additionally, a feedback system allows users to report issues and provide input for continuous improvements. By integrating secure authentication, AI-driven assistance, predictive analytics, and location-based hospital search, this system enhances accessibility, reduces waiting times, and ensures a smooth and organized appointment booking process for both patients and healthcare providers. Index Terms—The Doctor Appointment System, Patient Mod- ule, Doctor Module, Administrator Module, Disease Classification Algorithm, Machine Learning, RASA Chatbot, Appointment Management, Doctor Recommendation.
Authors
- P. Das (ORCID: https://orcid.org/0000-0003-1147-6541)
- Swarangi Jog
- Roshani V. Bhaskarwar
- Suraj Gaikar
- Chitralekha Chavan
Publication Details
- Journal
- INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
- Published
- 2025-03-14
- DOI
- https://doi.org/10.55041/ijsrem42822
- Primary Topic
- Telemedicine and Telehealth Implementation
- Type
- article
- Field-Weighted Citation Impact
- 0.00