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Titel: Learning from machine learning: prediction of age-related athletic performance decline trajectories
VerfasserIn: Hoog Antink, Christoph
Braczynski, Anne K.
Ganse, Bergita
Sprache: Englisch
Titel: GeroScience
Bandnummer: 43
Heft: 5
Seiten: 2547–2559
Verlag/Plattform: Springer Nature
Erscheinungsjahr: 2021
Freie Schlagwörter: Artifcial intelligence
Track and field
Big data
Longevity
Ageing
Prediction
DDC-Sachgruppe: 610 Medizin, Gesundheit
Dokumenttyp: Journalartikel / Zeitschriftenartikel
Abstract: Factors that determine individual age-related decline rates in physical performance are poorly understood and prediction poses a challenge. Linear and quadratic regression models are usually applied, but often show high prediction errors for individual athletes. Machine learning approaches may deliver more accurate predictions and help to identify factors that determine performance decline rates. We hypothesized that it is possible to predict the performance development of a master athlete from a single measurement, that prediction by a machine learning approach is superior to prediction by the average decline curve or an individually shifted decline curve, and that athletes with a higher starting performance show a slower performance decline than those with a lower performance. The machine learning approach was implemented using a multilayer neuronal network. Results showed that performance prediction from a single measurement is possible and that the prediction by a machine learning approach was superior to the other models. The estimated performance decline rate was highest in athletes with a high starting performance and a low starting age, as well as in those with a low starting performance and high starting age, while the lowest decline rate was found for athletes with a high starting performance and a high starting age. Machine learning was superior and predicted trajectories with significantly lower prediction errors compared to conventional approaches. New insights into factors determining decline trajectories were identified by visualization of the model outputs. Machine learning models may be useful in revealing unknown factors that determine the age-related performance decline.
DOI der Erstveröffentlichung: 10.1007/s11357-021-00411-4
Link zu diesem Datensatz: urn:nbn:de:bsz:291--ds-353847
hdl:20.500.11880/32296
http://dx.doi.org/10.22028/D291-35384
ISSN: 2509-2723
2509-2715
Datum des Eintrags: 3-Feb-2022
Fakultät: M - Medizinische Fakultät
Fachrichtung: M - Chirurgie
Professur: M - Prof. Dr. med. Bergita Ganse
Sammlung:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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