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Computational models for detection of endocrinopathy in subfertile males

Authors :
Mark Sigman
Lawrence S. Ross
Antoine A. Makhlouf
R A Desai
Charles R. Powell
Jonathan P. Jarow
Craig Niederberger
Source :
International Journal of Impotence Research. 20:79-84
Publication Year :
2007
Publisher :
Springer Science and Business Media LLC, 2007.

Abstract

The observation that men with sperm density greater than 10 million/ml had low probability of endocrinopathy led to a refinement in the evaluation of subfertility. Using statistical methods, we sought to provide a more accurate prediction of which patients have an endocrinopathy, and to report the outcome as the odds of having disease. In addition, by examining the parameters that influenced the model significantly, the underlying pathophysiology might be better understood. Records of 1035 men containing variables including testis volume, sperm density, motility as well as the presence of endocrinopathy were randomized into 'training' and 'test' data sets. We modeled the data set using linear and quadratic discriminant function analysis, logistic regression (LR) and a neural network. Wilk's regression analysis was performed to determine which variables influenced the model significantly. Of the four models investigated, LR and a neural network performed the best with receiver operating characteristic areas under the curve of 0.93 and 0.95, respectively, correlating to a sensitivity of 28% and a specificity of 99% for the LR model, and a sensitivity and specificity of 56 and 97% for the neural network model. Reverse regression yielded P-values for the testis volume and sperm density of

Details

ISSN :
14765489 and 09559930
Volume :
20
Database :
OpenAIRE
Journal :
International Journal of Impotence Research
Accession number :
edsair.doi.dedup.....c2f6d4ac27a36462232e62e1442c69d0
Full Text :
https://doi.org/10.1038/sj.ijir.3901593