Machine learning for classifying chronic ankle instability based on ankle strength, range of motion, postural control and anatomical deformities in delivery service workers with a history of lateral ankle sprains

Abstract

Objective

Chronic ankle instability (CAI) frequently develops as a result of lateral ankle sprains (LAS) in delivery service workers (DSWs). Identifying risk factors for CAI is crucial for implementing targeted interventions. This study aimed to develop machine learning (ML) models for classifying CAI in DSWs with a history of LAS (DSWsLAS) and to identify key contributory factors.

Design

Exploratory, cross-sectional design.

Setting

and participants: A total of 121 DSWsLAS were screened for eligibility among 289 DSWs.

Methods

A total of 121 DSWsLAS were assessed for demographic characteristics, including ankle strength, range of motion, postural control, and anatomical deformities. Seven ML algorithms were trained and tested for classifying CAI. Principal component analysis (PCA) was used for feature extraction, and feature permutation importance (FPI) and Shapley additive explanations (SHAP) were employed to identify influential features.

Main outcome measures

Model performances were assessed using area under the curve (AUC). To interpret the classifications, we used FPI and SHAP values.

Results

PCA derived 7 principal components (PCs) accounting for 83.5% of the total variation in the data. The support vector machine (SVM) algorithm achieved the highest classifying performance (AUC = 0.817) among the ML models. FPI and SHAP revealed that PC1, PC2, PC5, and PC7 were the most influential features for classifying CAI in DSWsLAS.

Conclusions

The SVM algorithm, utilizing PCA-derived factors related to body mass index and ankle muscle strength demonstrated high classifying performance for diagnosis of CAI in DSWsLAS, emphasizing the importance of considering multiple contributory factors in the prevention and management of this condition.