Disease management: clustering-based disease prediction Online publication date: Thu, 09-Oct-2014
by Kulvaibhav Kaushik; Divya Kapoor; Vijayaraghavan Varadharajan; Rajarathnam Nallusamy
International Journal of Collaborative Enterprise (IJCENT), Vol. 4, No. 1/2, 2014
Abstract: The latest trends in healthcare industry have made enormous data available which is useful for disease prediction. This may be utilised by provider/payer for disease management study (DMS) for judging the present health condition and analysing disease trends. The present approach depends upon objective analysis which is based on binary value of symptoms and health indicators and direct relation between them. We propose a framework based on analytics to diagnose and predict diseases using enhanced genetic clustering using patient history, symptoms, and existing medical condition. New patients profile is analysed against existing patterns and mapped to a particular cluster based on the patient's medical parameters. After cluster formation, cohesion and adhesion between the clusters is determined, which helps to predict present medical condition and probability for being prone to a disease in future. This framework provides an efficient disease management based on the preliminary diagnosis of the patient.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Collaborative Enterprise (IJCENT):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook