Identifying discriminative features for diagnosis of Kashin-Beck disease among adolescents

• Published in: BMC musculoskeletal disorders 2021-09, Vol.22 (1), p.1-801, Article 801
• Main Authors: Zhang, Yanan, Wei, Xiaoli, Cao, Chunxia, Yu, Fangfang, Li, Wenrong, Zhao, Guanghui, Wei, Haiyan, Zhang, Feng'e, Meng, Peilin, Sun, Shiquan, Lammi, Mikko Juhani, Guo, Xiong
• Format: Article
• Language: English
• Subjects: Adolescents; Ankle; Arthritis; Classification; Demographic aspects; Diagnosis; Diagnostic Radiology; diagnostisk radiologi; Elbow; Feature selection; Finger; Kashin-Beck disease; Machine learning; Machine learning algorithms; Medical diagnosis; Medical examination; Metaphysis; Musculoskeletal diseases; Neural networks; Optimization algorithms; Orthopaedics; Orthopedics; ortopedi; Regression analysis; reumatologi; rheumatology; Sensitivity analysis; Support vector machines; Teenagers; Variables; Wrist; Youth
• ISSN: 1471-2474; 1471-2474
• DOI: 10.1186/s12891-021-04514-z

Introduction Diagnosing Kashin-Beck disease (KBD) involves damages to multiple joints and carries variable clinical symptoms, posing great challenge to the diagnosis of KBD for clinical practitioners. However, it is still unclear which clinical features of KBD are more informative for the diagnosis of Kashin-Beck disease among adolescent. Methods We first manually extracted 26 possible features including clinical manifestations, and pathological changes of X-ray images from 400 KBD and 400 non-KBD adolescents. With such features, we performed four classification methods, i.e., random forest algorithms (RFA), artificial neural networks (ANNs), support vector machines (SVMs) and linear regression (LR) with four feature selection methods, i.e., RFA, minimum redundancy maximum relevance (mRMR), support vector machine recursive feature elimination (SVM--RFE) and Relief. The performance of diagnosis of KBD with respect to different classification models were evaluated by sensitivity, specificity, accuracy, and the area under the receiver operating characteristic (ROC) curve (AUC). Results Our results demonstrated that the 10 out of 26 discriminative features were displayed more powerful performance, regardless of the chosen of classification models and feature selection methods. These ten discriminative features were distal end of phalanges alterations, metaphysis alterations and carpals alterations and clinical manifestations of ankle joint movement limitation, enlarged finger joints, flexion of the distal part of fingers, elbow joint movement limitation, squatting limitation, deformed finger joints, wrist joint movement limitation. Conclusions The selected ten discriminative features could provide a fast, effective diagnostic standard for KBD adolescents. Keywords: Kashin-Beck disease, Machine learning algorithms, Feature selection, Adolescents, Diagnosis