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PubMed · 15592067

Computational models for detection of erectile dysfunction.

Moshe Wald·Allen D Seftel·Lawrence S Ross·Mamdouh A B Mohamed·Craig S Niederberger
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Abstract

PURPOSE: Vascular comorbidities are well known to correlate with erectile dysfunction (ED) but a correlation with hypogonadism and depression is less clear. Using several linear and nonlinear mathematical models we investigated the correlation of age, hypogonadism and depression with ED using the Sexual Health Inventory for Men (SHIM) as a surrogate marker for ED. MATERIALS AND METHODS: A data set of 140 exemplars containing the input features age, total testosterone in ng/dl, Center for Epidemiologic Studies Depression scale score and output SHIM score (thresholded to 10 for moderate ED) was randomized into a modeling (training) set of 105 and a cross-validation (test) set of 35 with similar outcome frequencies preserved in each set. Using neUROn++, a set of C++ programs that we developed using the Cygwin (Red Hat, Raleigh, North Carolina) GNU C++ port for Windows (Microsoft, Redmond, Washington) distributed across Pentium (Intel, Santa Clara, California) platforms we modeled the data set using the linear methods, linear and quadratic discriminant function analysis, and logistic regression, and the nonlinear method of neural computation with several investigated architectures. RESULTS: A 4 hidden node network was found to have the highest accuracy compared to linear and quadratic discriminant function analyses, and logistic regression. ROC areas for the test set were 0.702, 0.645, 0.676 and 0.618, respectively. Analysis of the neural network demonstrated that moderate ED correlated with patient age and depression score. Forward and reverse regression of the neural network based on Wilk's generalized likelihood ratio test revealed that age was most significant (p <0.001), followed by Center for Epidemiologic Studies Depression Scale score (p <0.03), followed by testosterone (p >0.6). CONCLUSIONS: We investigated linear and nonlinear computational models of moderate ED. To our knowledge this is the first demonstration that SHIM correlates with age and a depression metric. Furthermore, moderate ED based on SHIM with a correlation with age may now provide a rationale and basis for future investigation into the understanding of age related erectile pathophysiology.

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AdultAge FactorsAgedAged, 80 and overComorbidityComputational BiologyDepressionDiscriminant AnalysisErectile DysfunctionHumansHypogonadismMaleMiddle AgedNeural Networks, ComputerROC CurveJournal ArticleResearch Support, Non-U.S. Gov'tResearch Support, U.S. Gov't, P.H.S.

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Moshe Wald, Allen D Seftel, Lawrence S Ross, Mamdouh A B Mohamed, Craig S Niederberger. 2005. Computational models for detection of erectile dysfunction.. https://doi.org/10.1097/01.ju.0000146839.43427.0b

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