• Keshav Lakhpati
• Aditi Morye
• Mitali Jain
• Ketan Juare
• Prachi Bhure

Navigating the healthcare system can be daunting for patients in search of suitable doctors. This project introduces a doctor recommendation system that utilizes data and algorithms to simplify this process. The system is designed to empower patients by providing personalized recommendations tailored to their specific needs and preferences. By considering factors like doctor expertise, experience, patient reviews, location, communication style, and insurance compatibility, the system aims to offer comprehensive and relevant suggestions. The integration of machine learning algorithms, such as collaborative filtering and content-based filtering, enhances the accuracy of these recommendations. The user-friendly interface facilitates easy interaction, allowing patients to input symptoms, apply filters, and explore doctor profiles seamlessly. This approach, rooted in simplicity and technical precision, strives to bridge the gap in healthcare decision-making.

Recommendation System, Healthcare, Diseases, Doctor Recommendation, Machine Learning, Data Science, Disease Prediction, Symptoms Analysis, Big Data Mining

"Centralized Doctor Healthcare Recommendation System: Based on Patient Evidence ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.h555-h561, March-2024, Available :http://www.jetir.org/papers/JETIR2403773.pdf

"Centralized Doctor Healthcare Recommendation System: Based on Patient Evidence ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. pph555-h561, March-2024, Available at : http://www.jetir.org/papers/JETIR2403773.pdf