Comparison of optimized machine learning approach to the understanding of medial tibial stress syndrome in male military personnel

BMC Research Notes

Research Note

Masoud Arabfard¹  Zeynab Ebrahimpour²  Abolfazl Shakibaee³  Vahid Sobhani³  Alireza Asgari³ 

[1] Chemical Injuries Research Center, Systems Biology and Poisoning Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran;Department of Physical Education and Sport Sciences, North Tehran Branch, Islamic Azad University, Tehran, Iran;Exercise Physiology Research Center, Lifestyle Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran;

关键词: Bayesian optimization;    Anthropometric predictors;    Injury risk;    Predictive methods;   

DOI  :  10.1186/s13104-023-06404-0

 received in 2023-02-08, accepted in 2023-06-18,  发布年份 2023

来源: Springer

【 摘 要 】

PurposeThis study investigates the applicability of optimized machine learning (ML) approach for the prediction of Medial tibial stress syndrome (MTSS) using anatomic and anthropometric predictors.MethodTo this end, 180 recruits were enrolled in a cross-sectional study of 30 MTSS (30.36 ± 4.80 years) and 150 normal (29.70 ± 3.81 years). Twenty-five predictors/features, including demographic, anatomic, and anthropometric variables, were selected as risk factors. Bayesian optimization method was used to evaluate the most applicable machine learning algorithm with tuned hyperparameters on the training data. Three experiments were performed to handle the imbalances in the data set. The validation criteria were accuracy, sensitivity, and specificity.ResultsThe highest performance (even 100%) was observed for the Ensemble and SVM classification models while using at least 6 and 10 most important predictors in undersampling and oversampling experiments, respectively. In the no-resampling experiment, the best performance (accuracy = 88.89%, sensitivity = 66.67%, specificity = 95.24%, and AUC = 0.8571) was achieved for the Naive Bayes classifier with the 12 most important features.ConclusionThe Naive Bayes, Ensemble, and SVM methods could be the primary choices to apply the machine learning approach in MTSS risk prediction. These predictive methods, alongside the eight common proposed predictors, might help to more accurately calculate the individual risk of developing MTSS at the point of care.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

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