Szczegóły publikacji
Opis bibliograficzny
Influence of neural network structure and data-set size on its performance in the prediction of height of growth hormone-treated patients / Urszula SMYCZYŃSKA, Joanna Smyczyńska, Ryszard TADEUSIEWICZ // Bio-Algorithms and Med-Systems / Jagiellonian University. Medical College ; ISSN 1895-9091. — 2016 — vol. 12 iss. 2, s. 53–59. — Bibliogr. s. 59, Abstr. — Publikacja dostępna online od: 2016-05-11
Autorzy (3)
- AGHSmyczyńska Urszula
- Smyczyńska Joanna
- AGHTadeusiewicz Ryszard
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 104405 |
|---|---|
| Data dodania do BaDAP | 2017-03-11 |
| DOI | 10.1515/bams-2016-0001 |
| Rok publikacji | 2016 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | Bio-Algorithms and Med-Systems |
Abstract
It is well known that the structure of neural network and the amount of available training data influence the accuracy of developed models; however, the exact character of this relation depends on the chosen problem. Thus, it was decided to analyze what impact these parameters have on the solution of the problem on which we work – the prediction of final height of children treated with growth hormone. It was observed that multilayer perceptron with a wide range of numbers of hidden neurons (from 1 to 100) could solve the problem almost equally well. Thus, this task seems to be rather simple, not requiring complex models. Larger networks tended to produce less accurate results and did not generalize well while working with the data not used in training. Repeating the experiment with the training data set reduced to 50% of its original content, as expected, caused a decrease in accuracy.
