NDLI: Prediction of subsequent fragility fractures: application of machine learning

Content Provider	Springer Nature : BioMed Central
Author	Zabihiyeganeh, Mozhdeh Mirzaei, Alireza Tabrizian, Pouria Rezaee, Aryan Sheikhtaheri, Abbas Kadijani, Azade Amini Kadijani, Bahare Amini Sharifi Kia, Ali
Abstract	Background Machine learning (ML) has shown exceptional promise in various domains of medical research. However, its application in predicting subsequent fragility fractures is still largely unknown. In this study, we aim to evaluate the predictive power of different ML algorithms in this area and identify key features associated with the risk of subsequent fragility fractures in osteoporotic patients. Methods We retrospectively analyzed data from patients presented with fragility fractures at our Fracture Liaison Service, categorizing them into index fragility fracture (n = 905) and subsequent fragility fracture groups (n = 195). We independently trained ML models using 27 features for both male and female cohorts. The algorithms tested include Random Forest, XGBoost, CatBoost, Logistic Regression, LightGBM, AdaBoost, Multi-Layer Perceptron, and Support Vector Machine. Model performance was evaluated through 10-fold cross-validation. Results The CatBoost model outperformed other models, achieving 87% accuracy and an AUC of 0.951 for females, and 93.4% accuracy with an AUC of 0.990 for males. The most significant predictors for females included age, serum C-reactive protein (CRP), 25(OH)D, creatinine, blood urea nitrogen (BUN), parathyroid hormone (PTH), femoral neck Z-score, menopause age, number of pregnancies, phosphorus, calcium, and body mass index (BMI); for males, the predictors were serum CRP, femoral neck T-score, PTH, hip T-score, BMI, BUN, creatinine, alkaline phosphatase, and spinal Z-score. Conclusion ML models, especially CatBoost, offer a valuable approach for predicting subsequent fragility fractures in osteoporotic patients. These models hold the potential to enhance clinical decision-making by supporting the development of personalized preventative strategies.
Related Links	https://bmcmusculoskeletdisord.biomedcentral.com/counter/pdf/10.1186/s12891-024-07559-y.pdf
Ending Page	10
Page Count	10
Starting Page	1
File Format	HTM / HTML
ISSN	14712474
DOI	10.1186/s12891-024-07559-y
Journal	BMC Musculoskeletal Disorders
Issue Number	1
Volume Number	25
Language	English
Publisher	BioMed Central
Publisher Date	2024-06-04
Access Restriction	Open
Subject Keyword	Orthopedics Rehabilitation Rheumatology Sports Medicine Internal Medicine Epidemiology Osteoporosis Fragility fracture Subsequent fracture Machine learning Data mining
Content Type	Text
Resource Type	Article
Subject	Orthopedics and Sports Medicine Rheumatology
Journal Impact Factor	2.2/2023
5-Year Journal Impact Factor	2.6/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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