Application of Artificial Neural Networks and Principal Component Analysis to Predict Results of Infertility Treatment Using the IVF Method
| Autor: | Dorota Jankowska, Allen Morgan, Anna Justyna Milewska, Slawomir Wolczynski, Teresa Więsak, Robert Milewski, Urszula Cwalina, Dorota Citko |
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| Rok vydání: | 2016 |
| Předmět: |
Infertility
030219 obstetrics & reproductive medicine Artificial neural network business.industry Computer Science::Neural and Evolutionary Computation 06 humanities and the arts 0603 philosophy ethics and religion Machine learning computer.software_genre medicine.disease 03 medical and health sciences Philosophy 0302 clinical medicine AZ20-999 060302 philosophy Principal component analysis Medicine History of scholarship and learning. The humanities Artificial intelligence business computer |
| Zdroj: | Studies in Logic, Grammar and Rhetoric, Vol 47, Iss 1, Pp 33-46 (2016) |
| ISSN: | 2199-6059 0860-150X |
| Popis: | There are high hopes for using the artificial neural networks (ANN) technique to predict results of infertility treatment using the in vitro fertilization (IVF) method. Some reports show superiority of the ANN approach over conventional methods. However, fully satisfactory results have not yet been achieved. Hence, there is a need to continue searching for new data describing the treatment process, as well as for new methods of extracting information from these data. There are also some reports that the use of principal component analysis (PCA) before the process of training the neural network can further improve the efficiency of generated models. The aim of the study herein presented was to verify the thesis that the use of PCA increases the effectiveness of the prediction by ANN for the analysis of results of IVF treatment. Results for the PCA-ANN approach proved to be slightly better than the ANN approach, however the obtained differences were not statistically significant. |
| Databáze: | OpenAIRE |
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