Comparative analysis of decision tree algorithms for treatment prediction in wrist bone injuries
DOI:
https://doi.org/10.5281/zenodo.23929595Keywords:
data mining, decision trees, C4.5, ID3, wrist injuries, clinical decision support systemsAbstract
Wrist bone injuries, particularly distal radius fractures and trauma-related arthralgia, create substantial clinical demand and require timely therapeutic decisions. This study adapts and strengthens a comparative decision-tree model aimed at recommending treatments for adult patients with wrist injuries using retrospective clinical records from 3,360 patients treated between 2023 and 2024. The methodology integrates the KDD process, record cleaning, clinical-variable encoding, cross-validation, and evaluation through confusion matrices, classification metrics, and ROC curves. ID3 and C4.5/J48 were compared because of their interpretability and potential use in clinical decision-support systems. Results show that C4.5 outperformed ID3, achieving 99.98% accuracy, 0.02% error rate, 99.50% F1-score, and 0.94 AUC-ROC, compared with 95.23%, 4.77%, 94.10%, and 0.88 for ID3. The C4.5 tree selected injury severity as the root node, followed by pain type and age, producing clinically understandable rules for recommending rest, physical therapy, medication, or surgery. C4.5 is therefore a robust and interpretable alternative for supporting personalized therapeutic recommendations, provided that external validation is performed and the model is used as a complement to medical judgment rather than a replacement for it.
Keywords: data mining, decision trees, C4.5, ID3, wrist injuries, clinical decision support systems
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Copyright (c) 2026 Edgar Corona Organiche, Brandon Moya Bucio, Abraham-Jorge Jiménez-Alfaro, Griselda Cortés-Barrera, Norma-Karen Valencia-Vázquez
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