An AI-powered healthcare system that combines machine learning, appointment scheduling, and chatbot assistance to enhance patient care and healthcare workflow efficiency.
To build an intelligent healthcare system that enables:
- Online appointment scheduling with doctors
- AI-based chatbot for patient queries and symptom checks
- Disease prediction using machine learning models
- Data-driven risk profiling and decision support for doctors
- Python
- PySpark
- Big Data
- Scikit-learn, XGBoost, Pandas, NumPy
- Jupyter Notebook
- MySQL
Datasets/– Healthcare datasets used for trainingnotebooks/– EDA, training, and evaluation notebooksappointment/– Modules for scheduling and bookingchatbot/– Scripts or integration code for AI chatbotREADME.md– Project overview and usage
- Appointment scheduling with time slot management
- Chatbot interface for answering patient FAQs and symptom triage
- Disease prediction models (e.g., diabetes, heart disease)
- Clean UI and backend for managing patients and appointments
- Scalable for clinics, hospitals, and remote care systems
- Voice-enabled chatbot support
- Real-time doctor availability
- Secure user authentication and role-based access
- API integration with wearable health devices
All datasets used in this project are self-created and curated specifically for healthcare use cases. They are structured to support:
- Machine Learning tasks such as disease prediction and patient risk profiling
- Chatbot training for understanding common patient queries, symptoms, and medical terminology
- Appointment-related workflows including doctor availability and scheduling patterns
These datasets reflect realistic healthcare scenarios and are tailored for research, development, and educational purposes within the scope of this project.
Created by Rakesh — feel free to connect or contribute to the project!