Pregled bibliografske jedinice broj: 747916
Analyzing growth: A data-driven approach
Analyzing growth: A data-driven approach // Abstract Proceedings XIII International Congress of Human Growth and Clinical Auxology / Bigec, Martin (ur.). - Maribor, Slovenija : ISGA International Association for the Study of Human Growth and Clinical Auxology / Bigec, Martin (ur.).
Maribor: ISGA International Association for the Study of Human Growth and Clinical Auxology, 2014. str. 25-25 (predavanje, međunarodna recenzija, sažetak, znanstveni)
CROSBI ID: 747916 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Analyzing growth: A data-driven approach
Autori
Barbieri, Davide ; Sindik, Joško ; Matovinović Osvatić Martina ; Ferriani, Stefano
Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni
Izvornik
Abstract Proceedings XIII International Congress of Human Growth and Clinical Auxology / Bigec, Martin (ur.). - Maribor, Slovenija : ISGA International Association for the Study of Human Growth and Clinical Auxology
/ Bigec, Martin - Maribor : ISGA International Association for the Study of Human Growth and Clinical Auxology, 2014, 25-25
ISBN
978-961-6909-34-1
Skup
Human Growth, Chronic Diseease, and Population Health ; Child, Sports and Growth
Mjesto i datum
Maribor, Slovenija, 17.09.2014. - 20.09.2014
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
data mining; forecasting
Sažetak
Aim of this study was to provide a data-driven methodology and framework to auxology, in order for researchers and medical doctors to benefit from large data repositories, from which it is possible to extract hidden, meaningful - but often counterintuitive or unpredicted– information, or to corroborate current knowledge. A large data set of 41, 085 young athletes both genders, aged 7-17, engaged in chosen individual and team sport disciplines, containing clinical and anthropometric data, was investigated by means of traditional statistical methods and innovative data mining/machine learning algorithms. In particular, classification algorithms like decision tree have been adopted in order to make predictions on different biomedical variables. Data mining correctly classified individuals in certain age groups, of both genders, who are engaged in two categories of sports. In case of large samples, data mining has proved to be an effective predictive and decision support tool, which can be used to improve data analysis, beside traditional statistical techniques.
Izvorni jezik
Engleski
Znanstvena područja
Javno zdravstvo i zdravstvena zaštita, Etnologija i antropologija
POVEZANOST RADA
Ustanove:
Institut za antropologiju
