Open Access Open Access  Restricted Access Subscription or Fee Access

Disease Prediction Android Application using Machine Learning

Sneha Rajendra Bedse, Menika Mahendra Prasad, Vrushali Pankaj Thakur

Abstract


The rapid proliferation of internet technology and handled devices has opened up new avenues for an online healthcare system. There are instances where online medical help or healthcare advice is easier and faster to grasp than real world help. People often feel reluctant to go to hospital or visit doctors for minor symptoms. However, in many cases, these minor symptoms may trigger into a major health hazard. As online wellbeing counsel is effectively reachable, it very well may be an extraordinary head start for clients. Besides, existing on the web medical services frameworks experience, the ill effects of an absence of unwavering quality and precision. An android application is developed for users for easy portability, configuring and being able to access remotely where doctors cannot reach easily. This application analyzes the symptoms provided by the user as input and gives the most accurate probable disease output to user. This application also looks forward by providing information like description about predicted disease and measures to be taken by users of a particular predicted disease. Expectation is finished by carrying out the Naive Bayes Classifier.


Keywords


Naive Bayes algorithm, symptoms, android app, machine learning, disease prediction

Full Text:

PDF

Refbacks

  • There are currently no refbacks.