Analysing Big Data on Health Management Occupancy Rates with Decision Tree Algorithms

Abstract

ABSTRACT Objective: This study examines the declining occupancy rates in Health Management departments due to the proliferation of such programs in higher education institutions. Methods: This study analyzes data from 113 departments in state and foundation universities in 2021 utilizing decision tree analysis. The research investigates key factors influencing occupancy rates, including the type of education, number of academic staff, year of establishment, and the geographical location of the university. The analysis employs SPSS, Clementine Data Mining Module, and CHAID and CART Decision Tree Algorithms to determine the significance of these factors. Results: The primary determinants influencing occupancy rates are educational institution type, number of academic personnel, year of establishment, and geographical location within urban centers. Conclusion: The findings of this study are anticipated to provide guidance for researchers, decision-makers, and health administrators in the field of health management. Furthermore, this research may serve as a foundation for future studies and initiatives aimed at enhancing the employment prospects of health management graduates, thereby ensuring that the administration of healthcare institutions is entrusted to individuals with specialized education in health management.

Keywords

• Big Data
• Health Management
• Decision Tree
• Data Mining

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