Using Data Mining Principles in Implementing Predictive Analytics to Different Areas

Abstract

This study delves into the realm of information-based knowledge discovery technologies and underscores the growing necessity for extensive data representation to enhance the management of care and mitigate the financial costs associated with promoting long-term care. The proliferation of information collected and disseminated through the Internet has reached unprecedented levels in the context of long-term financial health statistics, posing a challenge for businesses to effectively leverage this wealth of data for research purposes. The explicit specification of costs becomes paramount when dealing with substantial volumes of data. Consequently, the literature on the application of big data in logistics is categorized based on the nature of methods employed, such as explanatory, predictive, regulatory, strategic, and operational approaches. This includes a comprehensive examination of how big data analysis is applied within large corporations. In the healthcare domain, the study contributes to the evaluation of usability by providing a framework to analyze the maturity of structures at four distinct levels. The emphasis is particularly on the pivotal role played by predictive analytics in the healthcare industry through big data methodologies. Furthermore, the study advocates for a paradigm shift in management's perception of large business data sets, urging them to view these as strategic resources that must be seamlessly integrated into the company. This integration is seen as imperative for achieving comprehensive business analysis and staying competitive in the ever-evolving landscape of healthcare. The study concludes by shedding light on the limitations inherent in the research and delineating the specific focus areas that have been addressed.