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

ABSTRACT:

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 r

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC8449456 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature