Influence of neural network structure and data-set size on its performance in the prediction of height of growth hormone-treated patients
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Urszula Smyczyńska
,
Joanna Smyczyńska
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.
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: None declared.
Employment or leadership: None declared.
Honorarium: None declared.
Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.
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©2016 by De Gruyter
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Articles in the same Issue
- Frontmatter
- Review
- Regeneration of knee joint menisci – methods review
- Original Articles
- Influence of neural network structure and data-set size on its performance in the prediction of height of growth hormone-treated patients
- Empirical wavelet transform-based delineator for arterial blood pressure waveforms
- Independent component analysis of EEG data for EGI system
- Model of dental braces for palatal suture expansion with tension analysis of two titanium alloys: Ti-13Nb-13Zr and Ti-6Al-4V
- A stepwise protocol for neural network modeling of persistent postoperative facial pain in chronic rhinosinusitis
- Short Communication
- Data mining with Random Forests as a methodology for biomedical signal classification
