• Abhijit Shendage
• Saurabh Gore
• Yash Patil
• Samyak Vaidya
• Aparna Kalaskar

People often fall sick, though everyone may not be able seek immediate medical help. As seen in the Covid-19 situation, it may not always be possible to consult the doctor. Persons with disabilities or people having serious medical conditions face difficulties in visiting doctors because of long queues and lengthy processes. It has been observed that the people in rural and remote areas face the same difficulties. As well, due to the newly normalized helter-skelter lifestyle, we choose to ignore our medical problems, or sometimes we may overvalue them. In such situations, many people take advice from unprofessional. This all might lead to some serious concerns. Therefore, this paper proposes a web application that uses modern machine learning algorithms to diagnose user symptoms. The proposed system also includes all the generalized healthcare features which can be virtualized. The system connects all healthcare-related stakeholders with one another, so as they can communicate with others to solve their respective problems. Patients can take medical opinions from medical professionals, order pharmaceuticals from nearby pharmacies. Our proposed framework implements 3 different machine learning techniques Naïve Bayes, K-Nearest Neighbors, Decision Tree Classifier. This may create a path of enormous development in the health care sector.

Disease Prediction, Django, Doctor, K-Nearest Neighbors, decision tree, healthcare, Machine Learning, Pharmacists

"Comprehensive Healthcare System Using ML", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 5, page no.g41-g47, May-2021, Available :http://www.jetir.org/papers/JETIR2105799.pdf

"Comprehensive Healthcare System Using ML", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 5, page no. ppg41-g47, May-2021, Available at : http://www.jetir.org/papers/JETIR2105799.pdf