ABSTRACT
One major challenge encountered during crime investigation via automated systems is the inability of conventional data analysis techniques to adequately handle the enormous data that are made available during the investigation. Existing crime investigation frameworks are built on orthodox data analysis techniques which cannot sufficiently manned the unprecedented size and variety of data available today, not to mention the significantly more anticipated data in the near future. This has affected the healthcare industry where data is predominantly multi-structured and is growing at a considerably faster rate. To address this, a big data analytics model based on deep learning was designed using enterprise application diagrams. This model is intended to be implemented using Apache Hadoop, a big data implementation framework. This model creates a platform that handles a phenomenon affecting millions of people world-wide. It provides security intelligence by shortening the time of correlating and deriving evidence from large volume of data during healthcare crime investigation. Finally, this research also enabled the healthcare systems to systematically use big data analytics to identify inefficiencies and best practices that improved care delivery and reduce costs.
Keywords: Crime, Hadoop, Deep Learning, Investigation Data Analytics, Health Insurance
