Open AccessSymptoms to Disease Mapping and Doctor Recommendation System
Author(s) -
Tapodhir Acharjee,
S. Chanda,
Suman Nunia,
Ananya Choudhury,
Sanjeev Kumar
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.a9495.109119
Subject(s) - python (programming language) , naive bayes classifier , computer science , disease , decision tree , classifier (uml) , bayes' theorem , recommender system , artificial intelligence , medicine , data mining , machine learning , pathology , bayesian probability , programming language , support vector machine
To find an appropriate doctor who is specialized to treat a certain disease while only symptoms are known is not easy job for the patients. In this paper, we describe a recommended framework to find the best doctors in accordance with patients' requirements. In the proposed system, first it considers only those doctors whose profile match with patients' requirements. Second, the best doctors will be recommended out of previously obtained doctors based on the parameter patients' feedback i.e., patients' review. Our proposal will suggest a doctor recommendation system that uses review mining technique, which can be used in those countries that have huge uneven distribution of medical resources. In our model we have used the decision tree for symptoms to disease mapping and Naive Bayes classifier for sentiment analysis which are connected to each other using a bridge of python logic and the required output is top doctors based on the users input