{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T09:45:42Z","timestamp":1785923142953,"version":"3.56.0"},"reference-count":35,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T00:00:00Z","timestamp":1785888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Children"],"abstract":"<jats:p>Background: The six-minute walk test (6MWT) assesses functional exercise capacity, but existing reference equations for children with obesity rely on traditional linear regression, potentially overlooking complex, non-linear relationships between anthropometric characteristics and functional exercise capacity. Objective: This study aimed to develop and internally validate machine-learning (ML) prediction models and preliminary prediction equations derived from explainable ML models for six-minute walk distance (6MWD) in Tunisian school-aged children with obesity and to compare their predictive performance with a conventional regression-based approach. Methods: We analyzed data from 236 school-aged children with obesity (104 females, 132 males; 6\u201312 years). Anthropometric measurements included body mass (BM), height, body mass index (BMI), waist circumference (WC), and hip circumference (HC). Five models were evaluated: linear regression, Ridge, Lasso, Elastic Net, and XGBoost. Performance was assessed using five-fold cross-validation and evaluated by the mean absolute error (MAE) and root mean square error (RMSE). Predictor importance was assessed using SHapley Additive exPlanation (SHAP) and Gini importance. Results: XGBoost achieved the best predictive performance, with the lowest MAE (17.4 \u00b1 2.5 m in females and 19.5 \u00b1 1.9 m in males) and RMSE (25.3 \u00b1 3.1 m in females and 26.4 \u00b1 2.2 m in males). Age was the strongest predictor across all models (SHAP: 54.2\u201362.0%; Gini importance: 0.52\u20130.69), followed by height and BMI. Sex-specific analyses indicated that, in females, age and BMI contributed ~80% to the cumulative SHAP analysis; whereas, in males, age, height, and WC were the primary factors. Conclusions: ML, particularly XGBoost, significantly improves 6MWD prediction in school-aged children with obesity compared with traditional linear regression. Explainable ML increases model interpretability by evaluating the relative importance of anthropometric predictors. These obesity-specific prediction models may better capture complex non-linear associations between anthropometrics and functional exercise capacity. These initial population-specific models should be validated in larger independent samples before routine use clinical or field settings.<\/jats:p>","DOI":"10.3390\/children13081043","type":"journal-article","created":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T08:14:46Z","timestamp":1785917686000},"page":"1043","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning-Based Prediction of Six-Minute Walk Distance in Children with Obesity"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-2941-1095","authenticated-orcid":false,"given":"Emna","family":"Makni","sequence":"first","affiliation":[{"name":"Research Laboratory of Exercise Physiology and Pathophysiology (LR19ES09), Faculty of Medicine Ibn Al-Jazzar, University of Sousse, Sousse 4002, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3751-2125","authenticated-orcid":false,"given":"Mohamed","family":"Elloumi","sequence":"additional","affiliation":[{"name":"Sport Sciences and Diagnostics Research Group, College of Sciences and Humanities, Prince Sultan University, Riyadh 11586, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0347-8053","authenticated-orcid":false,"given":"Achraf","family":"Ammar","sequence":"additional","affiliation":[{"name":"Department of Training and Movement Science, Institute of Sport Science, Johannes Gutenberg-University Mainz, 55122 Mainz, Germany"},{"name":"Research Laboratory, Molecular Bases of Human Pathology (LR19ES13), Faculty of Medicine of Sfax, University of Sfax, Sfax 3000, Tunisia"},{"name":"Department of Nutrition and Food Technology, School of Agriculture, The University of Jordan, Amman 11942, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8439-0429","authenticated-orcid":false,"given":"Mehdi Ben","family":"Brahim","sequence":"additional","affiliation":[{"name":"Sport Sciences and Diagnostics Research Group, College of Sciences and Humanities, Prince Sultan University, Riyadh 11586, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Younes","family":"Hachana","sequence":"additional","affiliation":[{"name":"Research Laboratory (LR23JS01) \u201cSport Performance, Health & Society\u201d, Higher Institute of Sport and Physical Education of Ksar Sa\u00efd, University of \u201cLa Manouba\u201d, Manouba 2037, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100375","DOI":"10.1016\/j.metop.2025.100375","article-title":"Obesity: Prevalence, causes, consequences, management, preventive strategies and future research directions","volume":"27","author":"Ahmed","year":"2025","journal-title":"Metab. 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