Authors: Kulvaibhav Kaushik; Divya Kapoor; Vijayaraghavan Varadharajan; Rajarathnam Nallusamy
Addresses: Infosys Limited, 3rd & 4th floor, Building-1, Plot No. IT-A-001-A-1, Mahindra World City, SEZ, Jaipur 302037, India ' 3D/168, Saket Nagar, Bhopal, Madhya Pradesh, 462024, India ' Infosys Limited, Bangalore 560100, India ' Embnology Solutions Private Limited, No. 35, Vignana Nagar Main Road, Bangalore 560037, India
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.
Keywords: disease management; disease prediction; disease analysis; clustering; healthcare management; big data analytics; preliminary diagnosis.
International Journal of Collaborative Enterprise, 2014 Vol.4 No.1/2, pp.69 - 82
Available online: 09 Oct 2014 *Full-text access for editors Access for subscribers Purchase this article Comment on this article