Introduction Frailty is a common condition in older adults with diabetes, which significantly increases the risk of adverse health outcomes. Early identification of frailty in this population is ...

In today’s data-driven world, simplicity often wins. One such tool that offers both power and interpretability is the Decision Tree, a fundamental machine learning algorithm that continues to play a ...

In order to improve the accuracy and efficiency of sports training data analysis, this paper proposes an optimized analysis model by combining Iterative Dichotomiser 3 (ID3) decision tree algorithm ...

Abstract: In this paper, we propose an in-node microprocessor-based vehicle classification approach to analyze and determine the types of vehicles passing over a 3-axis magnetometer sensor. Our ...

Decision tree is an effective supervised learning method for solving classification and regression problems. This article combines the Pearson correlation coefficient with the CART decision tree, ...

Abstract: Machine learning (ML) has been instrumental in solving complex problems and significantly advancing different areas of our lives. Decision tree-based methods have gained significant ...

Data mining is a crucial field in the era of big data, enabling the extraction of valuable patterns and knowledge from large datasets. Among the various techniques used in data mining, decision tree ...

Mellon, J., and Worrell, C., 2023: Explainability in Cybersecurity Data Science. Software Engineering Institute blog, Accessed June 24, 2026, https://doi.org/10.58012 ...

This article provides a birds-eye view on the role of decision trees in machine learning and data science over roughly four decades. It sketches the evolution of decision tree research over the years, ...

Machine learning holds the potential to solve many real-world problems, but interpretability is a necessary prerequisite for practitioners in high-stakes domains such as medicine and law. Decision ...