Journal article
Computational Models for Prediction of Intrauterine Insemination Outcomes
Journal of the Turkish German Gynecological Association, Vol.8(3), pp.302-307
09/01/2007
Abstract
Objective: Intrauterine insemination (IUI) using ejaculated sperm is a common option in the treatment of infertility of various etiologies. We sought to develop a computational model for the prediction of pregnancy following IUI.
Materials and Methods: A data set of 212 exemplars, derived from patients who underwent a first IUI cycle with ejaculated sperm, was divided into separate modelling and cross-validation sets, and analyzed retrospectively. The data set contained input features of maternal age, type of medication used for ovulation induction, semen volume, sperm concentration, motility and morphology and intra-uterine pregnancy output, and was modelled using various mathematical methods, including linear and radial support vector machines, linear and quadratic discriminant function analysis, logistic regression, and neural computation. Various models were used, in an attempt to achieve the highest model accuracy. A logistic regression model was found to have the highest accuracy, with a test set ROC area of 0.717.
Results: Forward regression of this model showed sperm morphology to be the most significant feature in predicting pregnancy (p=0.39), followed by maternal age (p=0.42), type of medication used for ovulation induction (p=0.6), sperm motility (p=0.61), semen volume (p=0.71) and sperm concentration (p=0.9). Reverse regression of the model revealed sperm motility to be the most significant feature in predicting pregnancy (p=0.37), followed by sperm morphology (p=0.39), maternal age (p=0.49), type of medication used for ovulation induction (0.61), sperm concentration (p=0.72) and semen volume (p=0.74).
Discussion: A logistic regression model of clinical relevance was developed, and is deployed on the World Wide Web for clinical use.
Details
- Title: Subtitle
- Computational Models for Prediction of Intrauterine Insemination Outcomes
- Creators
- Moshe Wald - Univ Iowa, Dept Urol, Iowa City, IA 52242 USAAmy E. T. Sparks - University of IowaBradley J. Van Voorhis - University of IowaCraig H. Syrop - University of IowaCriag S. Niederberger - Univ Illinois, Dept Urol, Chicago, IL 60607 USA
- Resource Type
- Journal article
- Publication Details
- Journal of the Turkish German Gynecological Association, Vol.8(3), pp.302-307
- Publisher
- Galenos Yayincilik
- ISSN
- 1309-0399
- eISSN
- 1309-0380
- Number of pages
- 6
- Language
- English
- Date published
- 09/01/2007
- Academic Unit
- Obstetrics and Gynecology; Urology
- Record Identifier
- 9984317106302771
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