Open AccessDisease Risk Prediction using SVM based on Geographical Location
Author(s) -
A. Abarna*,
A. Umamakeswari
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.24.11989
Subject(s) - support vector machine , computer science , similarity (geometry) , social media , the internet , collaborative filtering , big data , disease , health care , data mining , machine learning , artificial intelligence , data science , world wide web , recommender system , medicine , pathology , economics , image (mathematics) , economic growth
Nowadays, people get more useful information from the internet and other technological platforms like social media. Plenty of health-care related information is available in social media where people spend more time in it. The existing methodology doesn’t include location in particular the user similarity based on the attributes. The proposed method specifies the assessment of disease risk by Support Vector Machine (SVM) algorithm to identify the similarity between the users based on the geographical location and then recommends the health expert to the users. This method also identifies the fake users and validates them. The health-care associated with big-data can be performed effectively in the proposed framework. The experimental output shows that the proposed method is more effective when compared with Collaborative Filtering based Disease Risk Assessment.