Syed Imtiaz Ali Rizvi
Ajman University of Science and Technology, UAE
Syed Imtiaz Ali Rizvi is a senior lecture at Department of Information Systems, College of Information Technology, Ajman University of Science and Technology, Fujairah Campus, United Arab Emirates. He has bachelors degree in Electrical Engineering and Masters in Computer Science. He has more than 25 years of teaching experience and has supervised around 50 projects in a diversified fied of science and technolgy. He is extensify involved in research with Dr. Amer Al-Nasiri; who is Deputy Dean of the college and is a co-author of this paper. He has different published articles in the field of Image Processing and Data mining.
Healthcare information are traditionally collected through surveys, which are although a direct source but much of the healthcare information is hidden. There should be an indirect way to collect the healthcare information about individuals and communities. Such information provide a real-time insight into the health situation of individuals or communities. Currently Data warehousing is a common source in Businesses to get information to plan and to know the current and future trends in business. The main source of business data are individuals and communities; so why not this huge reservoir of information is used for healthcare. In this article we describe two analytical techniques based on support vector machines (SVMs) for data analysis and support vector regression (SVR) to extract and classify healthcare information from a typical Business data warehouse about individuals and communities. Working out on data collection of a local chain of retail market in UAE and from the purchase habits of consumers get their healthcare information. Different kernels are be used in Support of Vector Machines models. These include linear, polynomial, radial basis function (RBF) and sigmoid as part of mapping system and analysis of healthcare information. The results show that using SVM method as analytical and classification tool for healthcare data is promising and comparable to other techniques like ANN. Finally this technique can be used to correlate the extracted information with the existing standards of International health organizations at a national and global level and suggest the change in purchase habits of individuals and communities in context of healthcare.