Application of the Method of Representation of Decision Rules in a Hierarchical Structure for Forecasting and Data Analysis

Diabetes mellitus stands as a critical global health challenge in the twenty-first century, with its escalating prevalence impacting individuals worldwide. Its complications significantly diminish both the quality of life and life expectancy, contributing to early disabilities and heightened mortality rates. Effective prediction of this disease holds immense value for its early diagnosis and subsequent management. This study focuses on comprehensive data analysis, emphasizing the pivotal role of predictive analytics. Employing decision tree methodology, specifically a hierarchical structure for representation of decision rules, the research explores the application of this approach in forecasting and data analysis within the realm of diabetes mellitus. By delving into this crucial area, the study aims to elucidate the efficacy of decision trees in disease prediction and diagnostic enhancement.