文献类型：期刊文献

英文题名：Identification of biomarkers for knee osteoarthritis through clinical data and machine learning models

作者：Chen, Wei[1,2];Zheng, Haotian[3];Ye, Binglin[2];Guo, Tiefeng[2];Xu, Yude[2];Fu, Zhibin[2];Ji, Xing[2];Chai, Xiping[2];Li, Shenghua[2];Deng, Qiang[2]

第一作者：Chen, Wei;陈伟

通信作者：Li, SH[1];Deng, Q[1]

机构：[1]Gansu Univ Chinese Med, Clin Coll Chinese Med, Lanzhou, Gansu, Peoples R China;[2]Tradit Chinese Med Hosp Gansu Prov, Dept Orthopaed, Guazhou St 418, Lanzhou 730050, Gansu, Peoples R China;[3]Heilongjiang Univ Tradit Chinese Med, Grad Sch, Harbin 150000, Heilongjiang, Peoples R China

第一机构：甘肃中医药大学

通信机构：[1]corresponding author), Tradit Chinese Med Hosp Gansu Prov, Dept Orthopaed, Guazhou St 418, Lanzhou 730050, Gansu, Peoples R China.

年份：2025

卷号：15

期号：1

起止页码：1703

外文期刊名：SCIENTIFIC REPORTS

收录：;Scopus(收录号：2-s2.0-85215570070);WOS:【SCI-EXPANDED(收录号:WOS:001396240600031)】；

基金：The authors express our most sincere gratitude to the High-level Key Discipline Construction Project of State Administration of Traditional Chinese Medicine(Number:203), Guiding planning project of Lanzhou Science and Technology Bureau(2023-ZD-38), Gansu Provincial Youth Science and Technology Fund(24JRRA1042), The Construction Project of the 2024 Gansu Provincial Clinical Advantage Specialty (Orthopedics)(Number:56).

语种：英文

外文关键词：Knee osteoarthritis; Clinical data; Machine learning; Diagnostic performance

摘要：Knee osteoarthritis (KOA) represents a progressive degenerative disorder characterized by the gradual erosion of articular cartilage. This study aimed to develop and validate biomarker-based predictive models for KOA diagnosis using machine learning techniques. Clinical data from 2594 samples were obtained and stratified into training and validation datasets in a 7:3 ratio. Key clinical features were identified through differential analysis between KOA and control groups, combined with least absolute shrinkage and selection operator (LASSO) regression. The SHapley Additive Planning (SHAP) method was employed to rank feature importance quantitatively. Based on these rankings, predictive models were constructed using Logistic Regression (LR), Random Forest (RF), eXtreme Gradient Boosting (xGBoost), Naive Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT) algorithms. Models were developed for subsets of variables, including the top 5, top 10, top 15, and all identified features. Receiver operating characteristic (ROC) curves were applied to compare diagnostic performance across models. Additionally, a risk stratification framework for KOA prediction was designed using recursive partitioning analysis (RPA). Using difference analysis and LASSO, 44 critical clinical features were identified. Among these, age, plasma prothrombin time, gender, body mass index (BMI), and prothrombin time and international normalized ratio (PTINR) emerged as the top five features, with SHAP values of 0.1990, 0.0981, 0.0471, 0.0433, and 0.0422, respectively. Machine learning analysis demonstrated that these variables provided robust diagnostic performance for KOA. In the training set, area under the curve (AUC) values for LR, RF, xGBoost, NB, SVM, and DT models were 0.947, 0.961, 0.892, 0.952, 0.885, and 0.779, respectively. Similarly, in the validation dataset, these models achieved AUC values of 0.961, 0.943, 0.789, 0.957, 0.824, and 0.76. Among them, RF consistently exhibited superior diagnostic accuracy for KOA. Additionally, RPA analysis indicated a higher prevalence of KOA among individuals aged 54 years and older. The integration of the top five clinical variables significantly enhanced the diagnostic accuracy for KOA, particularly when employing the RF model. Moreover, the RPA model offered valuable insights to assist clinicians in refining prognostic assessments and optimizing clinical decision-making processes.