A Novel Big Data Reasoning enabled Predictive Model in Healthcare Domain for Disease Diagnosis

Big data is taking the digital world by a storm and it has its footprints in revolutionizing the healthcare sector. It can be used with various predictive learning based techniques to help in integrating frameworks and providing solutions regarding value based care to identify patients with high risk. It also helps in the diagnosis of the diseases with better statistical outcomes. This study discusses a novel proposed predictive model embedding big data analytics in healthcare domains. The model is based upon an intelligent disease risks detection using explanatory modules to find insights and linkages in data. Further feedback loops are used to gather input and makes modifications to the model to continuously enhance performance. The model provided scalability with a mean CPU usage and bandwidth utilization of 26.48% and 47.2% with increase in data size. Thus, the model can assist the medical experts in efficient and scalable health diagnosis.