Disease management: clustering-based disease prediction

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.