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Journals / Meandros Medical And Dental Journal / 2026 / Volume: 24 Issue: 2

Comparison of Machine Learning Methods to Predict Incomplete Atypical Femoral Fracture After Bisphosphonate Use in Postmenopausal Women

Sultan Turhan(MUGLA SITKI KOCMAN UNIVERSITY)
Tuğba Dübektaş Canbek(MUGLA SITKI KOCMAN UNIVERSITY)
Umut Canbek(MUGLA SITKI KOCMAN UNIVERSITY)
Eralp Doğu(MUGLA SITKI KOCMAN UNIVERSITY)
Pages
—
Publication Date
2023-06-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Conclusion: It is recommended to apply resampling methods in cases where there is an unbalanced class problem such as incomplete atypical femoral fracture.

📌 3 Key Takeaways

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Objective & Hypothesis:Objective: Long-term use of bisphosphonates (BP) for treating osteoporosis may cause incomplete atypical femoral fracture.
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Key Findings & Data:Results: We solved the class imbalance problem with the density-based synthetic minority over-sampling technique and found that the random forest and adaboost methods achieved the highest performance in the classification step.
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Conclusion & Impact:Conclusion: It is recommended to apply resampling methods in cases where there is an unbalanced class problem such as incomplete atypical femoral fracture.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Classification · unbalanced data · disease diagnosis

Abstract

Objective: Long-term use of bisphosphonates (BP) for treating osteoporosis may cause incomplete atypical femoral fracture. In this study, we compared the classification and risk estimation of incomplete atypical femoral fractures, which is an alternative approach to clinical risk assessment. Materials and Methods: A data set was randomly selected from women using postmenopausal BP. We identified a class imbalance problem in the population and created a balanced structure using the density-based synthetic minority over-sampling technique. We compared machine learning algorithms and conducted a case study. Results: We solved the class imbalance problem with the density-based synthetic minority over-sampling technique and found that the random forest and adaboost methods achieved the highest performance in the classification step. Conclusion: It is recommended to apply resampling methods in cases where there is an unbalanced class problem such as incomplete atypical femoral fracture. Ensemble methods perform better than traditional methods in this study.

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Cite This Article

Turhan, S., Canbek, T. D., Canbek, U., Doğu, E. (2023). Comparison of Machine Learning Methods to Predict Incomplete Atypical Femoral Fracture After Bisphosphonate Use in Postmenopausal Women. Meandros Medical And Dental Journal, Volume: 24 Issue: 2. https://doi.org/10.4274/meandros.galenos.2023.22043

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