References
- Bottaci, L., Drew, P. J., Hartley, J. E., Hadfield, M. B., Farouk, R., Lee, P. W. R., Macintyre, et al. (1997) Artificial neural networks applied to outcome prediction for colorectal cancer patients in separate institutions. The Lancet, 350(9076), 469–472.
- Buciński, A., Bączek, T., Krysiński, J., Szoszkiewicz, R., & Załuski, J. (2007). Clinical data analysis using artificial neural networks (ANN) and principal component analysis (PCA) of patients with breast cancer after mastectomy. Reports of Practical Oncology and Radiotherapy, 12(1), 9–17.
- Case, A. M. (2003). Infertility evaluation and management. Strategies for family physicians. Canadian Family Physician, 49, 1465–1472.
- Chen, D.-F., Ji, Q.-Ch., Zhao, L., & Zhang, H.-C. (2009). The Classification of Wine Based on PCA and ANN. In B. Cao, T.-F. Li, & C.-Y. Zhang. (Eds.), Advances in Intelligent and Soft Computing: Volume 62. Fuzzy Information and Engineering Volume 2 (pp. 647–655). Berlin Heidelberg: Springer.
- Dinç, E., Kanbur, M., & Baleanu, D. (2007). Comparative spectral analysis of veterinary powder product by continuous wavelet and derivative transforms. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 68(2), 225–230.
- Elrharras, A., El Moukhlis, S., Saadane, R., Wahbi, M., & Hamdoun, A. (2015). FPGA-Based Fully Parallel PCA-ANN for Spectrum Sensing. Computer and Information Science, 8(1), 108–118.
- Gonen, L. D. (2016). Satisfaction with in vitro fertilization treatment: patients’ experiences and professionals’ perceptions. Fertility Research and Practice, 2(6). DOI: 10.1186/s40738-016-0019-4
- Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143(1), 29–36.
- Hillier, T. A., Rousseau, A., Lange, C., Lépinay, P., Cailleau, M., Novak, M., Calliez, E., et al. (2006). Practical way to assess metabolic syndrome using a continuous score obtained from principal components analysis. Diabetologia, 49(7), 1528–1535.
- Ioele, G., De Luca, M., Dinç, E., Oliverio, F., & Ragno, G. (2011). Artificial Neural Network Combined with Principal Component Analysis for Resolution of Complex Pharmaceutical Formulations. Chemical and Pharmaceutical Bulletin, 59(1), 35–40
- Jackson, J. E. (1991). A user’s guide to principal components analysis. New York: Wiley.
- Jilani, T. A., Yasin, H., & Yasin, M. M. (2011). PCA-ANN for Classification of Hepatitis-C Patients. International Journal of Computer Applications, 14(7), 1–6.
- Jin-Mei, X., Xiao-Jian, W., & Ying-Jin, Y. (2007). Integration of wavelet transform with PCA and ANN for metabolomics data-mining. Metabolomics, 3, 531–537.
- Jolliffe, I. T. (2002). Principal Components Analysis (2nd Ed.). Berlin, Germany: Springer.
- Massart, D. L., & Vander Heyden, Y. (2004a). From tables to visuals: principal component analysis, part 1. LC-GC Europe, 17(11), 586–591.
- Massart, D. L., & Vander Heyden, Y. (2004b). From tables to visuals: principal component analysis, part 2. LC-GC Europe, 18(2), 84–89.
- Merchant, R., Gandhi, G., & Allahbadia, G. N. (2011). In vitro fertilization/intracytoplasmic sperm injection for male infertility. Indian Journal of Urology, 27(1), 121–132.
- Milewska, A. J., Jankowska, D., Citko, D., Więsak, T., Acacio, B., & Milewski, R. (2014). The use of principal component analysis and logistic regression in prediction of infertility treatment outcome. Studies in Logic, Grammar and Rhetoric. Logical, Statistical and Computer Methods in Medicine, 39(52), 7–23.
- Milewski, R., Jamiołkowski, J., Milewska, A. J., Domitrz, J., Szamatowicz, J., & Wołczyński, S. (2009). Prognosis of the IVF ICSI/ET procedure efficiency with the use of artificial neural networks among patients of the Department of Reproduction and Gynecological Endocrinology. Ginekologia Polska, 80(12), 900–906.
- Milewski, R., Milewska, A. J., Czerniecki, J., Leśniewska, M., & Wołczyński, S. (2013a). Analysis of the demographic profile of patients treated for infertility using assisted reproductive techniques in 2005–2010. Ginekologia Polska, 84(7), 609–614.
- Milewski, R., Milewska, A. J., Więsak, T., & Morgan, A. (2013b). Comparison of artificial neural networks and logistic regression analysis in pregnancy prediction using in the in vitro fertilization treatment Networks. Studies in Logic, Grammar and Rhetoric. Logical, Statistical and Computer Methods in Medicine, 35(48), 39–48.
- Morajda, J. (2000). Analiza głównych składowych jako metoda wstępnego przetwarzania danych dla sieci neuronowych. Zeszyty Naukowe, 551, 17–30.
- Oakley, L., Doyle, P., & Maconochie N. (2008). Lifetime prevalence of infertility and infertility treatment in the UK: results from a population based survey of reproduction. Human Reproduction, 23(2), 447–450.
- Pouliakis, A., Karakitsou, E., Margari, N., Bountris, P., Haritou, M., Panayiotides, J., Koutsouris, D., et al. (2016). Artificial Neural Networks as Decision Support Tools in Cytopathology: Past, Present, and Future. Biomedical Engineering and Computational Biology, 7, 1–18.
- Radwan, J. (2011). Epidemiologia niepłodności. In J. Radwan, & S. Wołczyński (Eds.), Niepłodność i rozród wspomagany (pp. 11–14). Poznań: Termedia.
- Rojas, R. (1996). Neural Networks: A Systematic Introduction. Berlin: Springer.
- Siristatidis, C., Pouliakis, A., Chrelias, C., & Kassanos, D. (2011). Artificial intelligence in IVF: A need. Systems Biology in Reproductive Medicine, 57(4), 179–185.
- Tadeusiewicz, R. (1993). Sieci neuronowe. Warszawa: Akademicka Oficyna Wydawnicza.
- Wilkes, S., Chinn, D. J., Murdoch, A., & Rubin, G. (2009). Epidemiology and management of infertility: a population-based study in UK primary care. Family Practice, 26(4), 269–274.