Adjusting decision threshold in naive bayes based IVF embryo selection;Naive bayes tabanli IVF embriyo seçiminde karar eşik deǧerinin Belirlenmesi

Proceedings of 2009 14th National Biomedical Engineering Meeting, BIYOMUT 2009

Volumn , Issue , 2009, Pages

  Uyar, Asli ^a   Bener, Ayşe ^a   Çiray, H Nadir ^b   Bahçeci, Mustafa ^b  

^a BOGAZICI UNIVERSITY   (Turkey)

^b Alman Hastanesi Bahçeci Tüp Bebek Merkezi ^*  (Turkey)

Author keywords

[No Author keywords available]

Indexed keywords

BINARY CLASSIFICATION PROBLEMS; CLASS DISTRIBUTIONS; DATA SETS; DECISION THRESHOLD; NAIVE BAYES; NAIVE BAYES CLASSIFIERS; ROC ANALYSIS;

BIOMEDICAL ENGINEERING;

EID: 70350244608     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BIYOMUT.2009.5130302     Document Type: Conference Paper

References (15)

1. P.C. Steptoe and R.G. Edwards, "Birth After Reimplantation of a Human Embryo" Lancet, vol. 2, p. 366,1978.
2. Gerris J, De Neubourg D. Single embryo transfer after IVF/ICSI: present possibilities and limits. J Obstet Gynecol India 2005;55:26-47.
3. Martikainen H, Orava M, Lakkakorpi J, Tuomivaara L. Day 2 elective single embryo transfer in clinical practice: Better outcome in ICSI cycles. Hum Reprod 2004;19:1364-1366.
4. Veleva, Z., Vilska, S., Hydn-Granskog, C., Tiitinen, A., Tapanainen, S. J., and Martikainen, H. Elective single embryo transfer in women aged 36-39 years. Hum Reprod 2006 Advance Access.
5. van Peperstraten, A., Hermens, R., Nelen, W., Stalmeier, P., Scheffer, G., Grol, R., and Kremer, J. Perceived barriers to elective single embryo transfer among ivf professionals: a national survey. Hum Reprod 2008;23:2718-2723.
6. Kononenko I. Machine learning for medical diagnosis: history, state of the art and perspective. Artif Intell Med 2001;23:89-109.
7. Baczkowski T, Kurzawa R, Glabowski, W. Methods of embryo scoring in in vitro fertilization. Reproductive Biology 2004;4:5-22.
8. Shen, S., Khabani, A., Klein, N., Battaglia, D.: Statistical Analysis of Factors Affecting Fertilization Rates and Clinical Outcome Associated with Intracytoplasmic Sperm Injection. American Society for Reproductive Medicine. 79 (2003)
9. Holte, J., Berglund, L., Milton, K., Garello, C., Gennarelli, G., Revelli, A., Bergh, T.: Construction of an Evidence Based Integrated Morphology Cleavage Embryo Score for Implantation Potential of Embryo Scored and Transferred on day 2 After Oocyte Retrieval. Human Reproduction. 22, 548-557 (2007)
10. Kaufmann SJ, Eastauh JL, Snowden S, Smye SW, Sharma V. The application of neural networks in predicting the outcome of in-vitro fertilization. Hum Reprod 1997;12:1454-1457.
11. Jurisica, I., Mylopoulos, J., Glasgow, J., Shapiro, H., Casper, R.F.: Case-Based Reasoning in IVF: Prediction and Knowledge Mining. Artificial Intelligence in Medicine. 12 (1998)
12. Saith, R., Srinivasan, A., Michie, D., Sargent, I.: Relationships Between the Developmental Potential of Human In-Vitro Fertilization Embryos and Features Describing the Embryo, Oocyte and Follicle. Human Reproduction Update. 4 ,121-134, (1998)
13. Morales, D.A., Bengoetxea, E., Larranaga, B., Garcia, M., Franco, Y., Fresnada, M., Merino, M.: Bayesian Classification for the Selection of In Vitro Human Embryos Using Morphological and Clinical Data.Computer Methods and Programs in Biomedicine. 90, 104-116, (2008)
14. Alpaydin E. Introduction to Machine Learning. MIT Press. 2004.
15. Maloof, M. (2003). Learning when data sets are imbalanced and when costs are unequal and unknown. In Proceedings of the ICML'03 workshop on learning from imbalanced data sets.