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"Young-Sang Kim"

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"Young-Sang Kim"

Original Articles
Application of Machine Learning Algorithms to Predict Osteoporotic Fractures in Women
Su Jeong Kang, Moon Jong Kim, Yang-Im Hur, Ji-Hee Haam, Young-Sang Kim
Korean J Fam Med 2024;45(3):144-148.   Published online January 29, 2024
DOI: https://doi.org/10.4082/kjfm.23.0186
Background
Predicting the risk of osteoporotic fractures is vital for prevention. Traditional methods such as the Fracture Risk Assessment Tool (FRAX) model use clinical factors. This study examined the predictive power of the FRAX score and machine-learning algorithms trained on FRAX parameters.
Methods
We analyzed the data of 2,147 female participants from the Ansan cohort study. The FRAX parameters employed in this study included age, sex (female), height and weight, current smoking status, excessive alcohol consumption (>3 units/d of alcohol), and diagnosis of rheumatoid arthritis. Osteoporotic fracture was defined as one or more fractures of the hip, spine, or wrist during a 10-year observation period. Machine-learning algorithms, such as gradient boosting, random forest, decision tree, and logistic regression, were employed to predict osteoporotic fractures with a 70:30 training-to-test set ratio. We evaluated the area under the receiver operating characteristic curve (AUROC) scores to assess and compare the performance of these algorithms with the FRAX score.
Results
Of the 2,147 participants, 3.5% experienced osteoporotic fractures. Those with fractures were older, shorter in height, and had a higher prevalence of rheumatoid arthritis, as well as higher FRAX scores. The AUROC for the FRAX was 0.617. The machine-learning algorithms showed AUROC values of 0.662, 0.652, 0.648, and 0.637 for gradient boosting, logistic regression, decision tree, and random forest, respectively.
Conclusion
This study highlighted the immense potential of machine-learning algorithms to improve osteoporotic fracture risk prediction in women when complete FRAX parameter information is unavailable.

Citations

Citations to this article as recorded by  
  • Machine learning is changing osteoporosis detection: an integrative review
    Yuji Zhang, Ming Ma, Xingchun Huang, Jinmin Liu, Cong Tian, Zhenkun Duan, Hongyin Fu, Lei Huang, Bin Geng
    Osteoporosis International.2025; 36(8): 1313.     CrossRef
  • Artificial intelligence in nutrition and ageing research – A primer on the benefits
    Pol Grootswagers, Tijl Grootswagers
    Maturitas.2025; 200: 108662.     CrossRef
  • Assessing the Risk of Osteoporotic Fracture Recurrence Using CT-based Radiomics and Machine Learning
    Xiaoyang Zheng, Caihong Zhu, Rui Zhang, Hongyu Sun
    Current Problems in Surgery.2025; : 101876.     CrossRef
  • AI-driven Technologies for Wrist Fracture Prediction: A Narrative Review of Emerging Approaches
    Stefania Briano, Maria Cesarina May, Giacomo Demontis, Giulia Pachera, Vittoria Mazzola, Federico Vitali, Alessandra Galuppi, Emanuela Dapelo, Andrea Zanirato, Matteo Formica
    Journal of Wrist Surgery.2025;[Epub]     CrossRef
  • Interpretable machine learning model for low bone density screening in older adults using demographic and anthropometric data: findings from 2005 to 2020 NHANES
    Weiyan Huang, Qimou Pan, Jiewei Peng, Yufeng Wu, Dawei Gao
    BMC Medical Informatics and Decision Making.2025;[Epub]     CrossRef
  • Clinical Applicability of Machine Learning in Family Medicine
    Jungun Lee
    Korean Journal of Family Medicine.2024; 45(3): 123.     CrossRef
  • Integrating Machine Learning for Personalized Fracture Risk Assessment: A Multimodal Approach
    Sheikh Mohd Saleem, Shah Sumaya Jan
    Korean Journal of Family Medicine.2024; 45(6): 356.     CrossRef
  • 5,732 View
  • 93 Download
  • 7 Web of Science
  • 7 Crossref
Sex Difference in the Association between Serum Homocysteine Level and Non-Alcoholic Fatty Liver Disease
Bo-Youn Won, Kyung-Chae Park, Soo-Hyun Lee, Sung-Hwan Yun, Moon-Jong Kim, Kye-Seon Park, Young-Sang Kim, Ji-Hee Haam, Hyung-Yuk Kim, Hye-Jung Kim, Ki-Hyun Park
Korean J Fam Med 2016;37(4):242-247.   Published online July 21, 2016
DOI: https://doi.org/10.4082/kjfm.2016.37.4.242
Background

The relationship between serum homocysteine levels and non-alcoholic fatty liver disease is poorly understood. This study aims to investigate the sex-specific relationship between serum homocysteine level and non-alcoholic fatty liver disease in the Korean population.

Methods

This cross-sectional study included 150 men and 132 women who participated in medical examination programs in Korea from January 2014 to December 2014. Patients were screened for fatty liver by abdominal ultrasound and patient blood samples were collected to measure homocysteine levels. Patients that consumed more than 20 grams of alcohol per day were excluded from this study.

Results

The homocysteine level (11.56 vs. 8.05 nmol/L) and the proportion of non-alcoholic fatty liver disease (60.7% vs. 19.7%) were significantly higher in men than in women. In men, elevated serum homocysteine levels were associated with a greater prevalence of non-alcoholic fatty liver disease (quartile 1, 43.6%; quartile 4, 80.6%; P=0.01); however, in females, there was no significant association between serum homocysteine levels and the prevalence of non-alcoholic fatty liver disease. In the logistic regression model adjusted for age and potential confounding parameters, the odds ratio for men was significantly higher in the uppermost quartile (model 3, quartile 4: odds ratio, 6.78; 95% confidential interval, 1.67 to 27.56); however, serum homocysteine levels in women were not associated with non-alcoholic fatty liver disease in the crude model or in models adjusted for confounders.

Conclusion

Serum homocysteine levels were associated with the prevalence of non-alcoholic fatty liver disease in men.

Citations

Citations to this article as recorded by  
  • Elevated Homocysteine is Associated With Liver Fibrosis in Metabolic Dysfunction–Associated Steatotic Liver Disease in a Sex- and Menopause-Specific Manner
    Mizuki Suzuki, Hwi Young Kim, Michael C. Reed, H. Frederik Nijhout, Allison Cruikshank, Manal Abdelmalek, Anna Mae Diehl, Paul M. Yen, Brijesh Kumar Singh, Madhulika Tripathi, Ayako Suzuki
    Gastro Hep Advances.2026; 5(1): 100800.     CrossRef
  • Homocysteine, folate, and nonalcoholic fatty liver disease: a systematic review with meta-analysis and Mendelian randomization investigation
    Shuai Yuan, Jie Chen, Lintao Dan, Ying Xie, Yuhao Sun, Xue Li, Susanna C Larsson
    The American Journal of Clinical Nutrition.2022; 116(6): 1595.     CrossRef
  • Genetic Association of Plasma Homocysteine Levels with Gastric Cancer Risk: A Two-Sample Mendelian Randomization Study
    Tianpei Wang, Chuanli Ren, Jing Ni, Hui Ding, Qi Qi, Caiwang Yan, Bin Deng, Juncheng Dai, Gang Li, Yanbing Ding, Guangfu Jin
    Cancer Epidemiology, Biomarkers & Prevention.2020; 29(2): 487.     CrossRef
  • Characterization of Early-Stage Alcoholic Liver Disease with Hyperhomocysteinemia and Gut Dysfunction and Associated Immune Response in Alcohol Use Disorder Patients
    Vatsalya Vatsalya, Khushboo S. Gala, Ammar Z. Hassan, Jane Frimodig, Maiying Kong, Nachiketa Sinha, Melanie L. Schwandt
    Biomedicines.2020; 9(1): 7.     CrossRef
  • Sex differences in risk factors for stroke in patients with hypertension and hyperhomocysteinemia
    Hui Pang, Qiang Fu, Qiumei Cao, Lin Hao, Zhenkun Zong
    Scientific Reports.2019;[Epub]     CrossRef
  • Association between homocysteine and non-alcoholic fatty liver disease in Chinese adults: a cross-sectional study
    Haijiang Dai, Weijun Wang, Xiaohong Tang, Ruifang Chen, Zhiheng Chen, Yao Lu, Hong Yuan
    Nutrition Journal.2016;[Epub]     CrossRef
  • 5,617 View
  • 52 Download
  • 9 Web of Science
  • 6 Crossref
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