The aim of this project is to help people to figure the disease they might have based on the symptoms in their bodies currently.
Instead of going with a neural network approach for our recommendation system, I decided to prioritize over the latency and time taken till output, and hence I built the recommendation system based on Multinomial Naïve Bayes algorithm, which although doesn’t provide accuracies as high as deep neural nets, can predict the disease in time to not halt the chat application and maintain the consistency of the system.
This project provides a novel method that uses machine learning technique, namely, Naïve Bayes classification algorithm for prediction of disease followed by recommendation of specialists of the predicted disease. Using medical profiles such as heart rate, blood pressure through sensors and other externally observable symptoms such as fever, cold, headache etc. that patient has, prediction of likelihood of a disease is done. Naïve Bayes algorithm takes these symptoms and predicts disease. Furthermore, all the needful and adequate information regarding the predicted disease as well as the recommended doctors is provided. Recommendation suggests the location, contact and other necessary details of the disease specialists based on the filters chosen by the user out of fewer fees, more experience, nearest location and feedback reviews of the doctors. Thus user can get appropriate treatment and necessary medical advice as fast as possible. Additionally, users provide their feedback for the recommended doctors which are then added for analysis in order to make further recommendations based on reviews.
A Naïve Bayesian classifier, is a model joint probability distribution over a set of stochastic variables. Instances of the classification problem under study are presented to the classifier as a combination of values for the feature variables. The classifier then returns a posterior probability distribution over the class variable. Learning such a classifier amounts to establishing the prior probabilities of the different classes and estimating the conditional probabilities of the various features given each of the classes. According to Bayes theorem of probability theory: - It is assumed that attributes E1 to Em are class conditionally independent, which means it is often assumed that after making the above assumption, the classifier is called Naïve Bayes.