Division of Critical Care Medicine, Department of Surgery, Ewha Womans University Mokdong Hospital, Ewha Womans University College of Medicine, Seoul, Korea.
Department of Surgery, Korea University College of Medicine, Seoul, Korea.
Health Informatics J. 2024 Jul-Sep;30(3):14604582241270778. doi: 10.1177/14604582241270778.
To assess the diagnostic utility of bone turnover markers (BTMs) and demographic variables for identifying individuals with osteoporosis. A cross-sectional study involving 280 participants was conducted. Serum BTM values were obtained from 88 patients with osteoporosis and 192 controls without osteoporosis. Six machine learning models, including extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), CatBoost, random forest, support vector machine, and k-nearest neighbors, were employed to evaluate osteoporosis diagnosis. The performance measures included the area under the receiver operating characteristic curve (AUROC), F1-score, and accuracy. After AUROC optimization, LGBM exhibited the highest AUROC of 0.706. Post F1-score optimization, LGBM's F1-score was improved from 0.50 to 0.65. Combining the top three optimized models (LGBM, XGBoost, and CatBoost) resulted in an AUROC of 0.706, an F1-score of 0.65, and an accuracy of 0.73. BTMs, along with age and sex, were found to contribute significantly to osteoporosis diagnosis. This study demonstrates the potential of machine learning models utilizing BTMs and demographic variables for diagnosing preexisting osteoporosis. The findings highlight the clinical relevance of accessible clinical data in osteoporosis assessment, providing a promising tool for early diagnosis and management.
为了评估骨转换标志物（BTMs）和人口统计学变量在识别骨质疏松症患者方面的诊断效用，进行了一项涉及 280 名参与者的横断面研究。从 88 名骨质疏松症患者和 192 名无骨质疏松症的对照者中获得了血清 BTM 值。使用了六种机器学习模型，包括极端梯度提升（XGBoost）、轻梯度提升机（LGBM）、CatBoost、随机森林、支持向量机和 k-最近邻，来评估骨质疏松症的诊断。性能指标包括接受者操作特征曲线（AUROC）下的面积、F1 分数和准确性。在 AUROC 优化后，LGBM 表现出最高的 AUROC 为 0.706。在 F1 分数优化后，LGBM 的 F1 分数从 0.50 提高到 0.65。结合三个最优模型（LGBM、XGBoost 和 CatBoost），AUROC 为 0.706，F1 分数为 0.65，准确性为 0.73。BTMs 以及年龄和性别被发现对骨质疏松症的诊断有显著贡献。本研究表明，利用 BTMs 和人口统计学变量的机器学习模型在诊断预先存在的骨质疏松症方面具有潜力。这些发现突出了在骨质疏松症评估中可获得的临床数据的临床相关性，为早期诊断和管理提供了有前途的工具。
