Machine Learning Solutions Information

Machine Learning Solutions was founded in 2014 by Gene D'Angelo of Boynton Beach, Florida. Gene has over 35 years of problem-solving experience, and over 11 years of advanced education in business, science and engineering. It is the combination of his skills and the power of the HOPS database system that makes possible the discovery of innovative paradigm shifts in the techniques utilized in the application of advanced analytics in big data environments. While working on his Ph.D. in Artificial Intelligence at the University of Central Florida in the early 1990's, Gene took a position as an internal consultant at Blue Cross and Blue Shield of Florida (BCBSFL). In that capacity, he first encountered the problems of big data in the Medicare Fraud Branch, where they were so deluged with claims data, the simplest statistical analysis was a challenge. Gene redesigned their analytical operations and implemented a network analysis algorithm that immediately discovered many millions of dollars in fraudulent operations, including a 200-million dollar fraud ring of 165 healthcare providers in south Florida. Following the success at BCBSFL, Gene joined HOPS International and pioneered a division with an emphasis on healthcare fraud detection. Over the next 15 years, he designed hundreds of big data algorithms for Medicare, Medicaid and large private health insurance companies. In addition to network analysis, Gene implemented many multivariate predictive models, trend analyses, pattern detection algorithms, and other methods to identify fraud, waste and abuse in healthcare. As the demand for big data analytics grew, it became obvious to Gene there was an unmet need to find new ways of implementing advanced analytics without the constraints of the general-purpose, memory- bound algorithms used in the past for smaller problems. He realized that the database software and iterative solution methodology, that had evolved within HOPS for advanced analytics on big data problems in healthcare, was generally applicable to ...