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

Sultan Turhan; Tuğba Dübektaş Canbek; Umut Canbek; Eralp Doğu

Makale detayları

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

Yazarlar:

Sultan Turhan | Tuğba Dübektaş Canbek | Umut Canbek | Eralp Doğu

Yayıncı:

Aydin Adnan Menderes University

Ana Başlıklar:

Clinical Oncology

Dergi:

Meandros Medical And Dental Journal Vol.24 Issue 2, pp.161-167

Belge Tipi:

Makale

Yayın Tarihi:

6/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001348390

pISSN:

2149-9063

Anahtar kelimeler:

Özet:

 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.

 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.

 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.

 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.

Makale detayları

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

Yazarlar:

Sultan Turhan | Tuğba Dübektaş Canbek | Umut Canbek | Eralp Doğu

Yayıncı:

Aydin Adnan Menderes University

Ana Başlıklar:

Clinical Oncology

Dergi:

Meandros Medical And Dental Journal Vol.24 Issue 2, pp.161-167

Belge Tipi:

Makale

Yayın Tarihi:

6/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001348390

pISSN:

2149-9063

Classification

unbalanced data

disease diagnosis

orthopedics

incomplete atypical fractures

Sayı

Makale detayları

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

Yazarlar: Sultan Turhan | Tuğba Dübektaş Canbek | Umut Canbek | Eralp Doğu

Yayıncı: Aydin Adnan Menderes University

Ana Başlıklar: Clinical Oncology

Dergi: Meandros Medical And Dental Journal Vol.24 Issue 2, pp.161-167

Belge Tipi: Makale

Yayın Tarihi: 6/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001348390

pISSN: 2149-9063

Classification

unbalanced data

disease diagnosis

orthopedics

incomplete atypical fractures

Sayı

Yazarlar:

Sultan Turhan | Tuğba Dübektaş Canbek | Umut Canbek | Eralp Doğu

Yayıncı:

Aydin Adnan Menderes University

Ana Başlıklar:

Clinical Oncology

Dergi:

Meandros Medical And Dental Journal Vol.24 Issue 2, pp.161-167

Belge Tipi:

Makale

Yayın Tarihi:

6/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001348390

pISSN:

2149-9063