The role of morphometric characteristics in predicting 20-meter sprint performance through machine learning

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dc.authorscopusid57873129600
dc.authorscopusid56416374000
dc.authorscopusid57441648700
dc.authorscopusid57225147593
dc.authorscopusid56779958100
dc.authorscopusid57203760014
dc.authorscopusid57142648900
dc.contributor.authorKurtoğlu A.
dc.contributor.authorEken Ö.
dc.contributor.authorÇiftçi R.
dc.contributor.authorÇar B.
dc.contributor.authorDönmez E.
dc.contributor.authorKılıçarslan S.
dc.contributor.authorJamjoom M.M.
dc.date.accessioned2024-08-04T20:03:30Z
dc.date.available2024-08-04T20:03:30Z
dc.date.issued2024
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThe aim of this study was to test the morphometric features affecting 20-m sprint performance in children at the first level of primary education using machine learning (ML) algorithms. In this study, 130 male and 152 female volunteers aged between 6 and 11 years were included. After obtaining demographic information of the participants, skinfold thickness, diameter and circumference measurements, and 20-m sprint performance were determined. The study conducted three distinct experiments to determine the optimal ML technique for predicting outcomes. Initially, the entire feature space was utilized for training the ML models to establish a baseline performance. In the second experiment, only significant features identified through correlation analysis were used for training and testing the models, enhancing the focus on relevant predictors. Lastly, Principal Component Analysis (PCA) was employed to reduce the feature space, aiming to streamline model complexity while retaining data variance. These experiments collectively aimed to evaluate different feature selection and dimensionality reduction techniques, providing insights into the most effective strategies for optimizing predictive performance in the given context. The correlation-based selected features (Age, Height, waist circumference, hip circumference, leg length, thigh length, foot length) has produced a minimum Mean Squared Error (MSE) value of 0.012 for predicting the sprint performance in children. The effective utilization of correlation analysis in the selection of relevant features for our regression model suggests that the features selected exhibit robust linear associations with the target variable and can be relied upon as predictors. © The Author(s) 2024.en_US
dc.description.sponsorshipPrincess Nourah Bint Abdulrahman University, PNU: PNURSP2024R104en_US
dc.description.sponsorshipThis research was funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2024R104), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.en_US
dc.identifier.doi10.1038/s41598-024-67405-y
dc.identifier.issn2045-2322
dc.identifier.issue1en_US
dc.identifier.pmid39025965en_US
dc.identifier.scopus2-s2.0-85198830903en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1038/s41598-024-67405-y
dc.identifier.urihttps://hdl.handle.net/11616/91877
dc.identifier.volume14en_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherNature Researchen_US
dc.relation.ispartofScientific Reportsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subject20-m sprint performanceen_US
dc.subjectChildrenen_US
dc.subjectCorrelation analysisen_US
dc.subjectMachine learning algorithmsen_US
dc.subjectMorphometric featuresen_US
dc.titleThe role of morphometric characteristics in predicting 20-meter sprint performance through machine learningen_US
dc.typeArticleen_US

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