Hospital and Doctor Recommendation with Disease Prediction Using Machine Learning

Abstract

Integration of doctor and hospital recommendations with sickness prediction analysis using machine learning assists the patients in finding suitable healthcare consultant based on their specific medical conditions. This application leverages a comprehensive dataset encompassing information about hospitals, doctors, patient medical records, and outcomes. By applying data pre-processing techniques, relevant features are extracted and selected to contribute to disease prediction and doctor recommendation. Deep learning models, decision trees, random forests, logistic regression, and other machine learning techniques which consists of Trained data to predict the specific diseases based on patient physical characteristics. The effectiveness of a doctor’s recommendation system is evaluated using the appropriate metrics and The trained models are put to use through a graphical user interface, like a web application or mobile app. The Advance Upgrade is achieved by adding the new data and incorporating the latest medical knowledge to the model.