Computational models for detection of erectile dysfunction

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

doi:10.1097/01.ju.0000146839.43427.0b

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Computational models for detection of erectile dysfunction

Journal article

Peer reviewed

Computational models for detection of erectile dysfunction

Moshe Wald, Allen D Seftel, Lawrence S Ross, Mamdouh A B Mohamed and Craig S Niederberger

The Journal of urology, Vol.173(1), pp.167-170

01/2005

DOI: 10.1097/01.ju.0000146839.43427.0b

PMID: 15592067

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Abstract

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. 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. 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). 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.

Adult

Age Factors

Aged

Aged, 80 and over

Comorbidity

Computational Biology

Depression - epidemiology

Discriminant Analysis

Erectile Dysfunction - diagnosis

Erectile Dysfunction - epidemiology

Humans

Hypogonadism - epidemiology

Male

Middle Aged

Neural Networks (Computer)

ROC Curve

Details

Title: Subtitle: Computational models for detection of erectile dysfunction
Creators: Moshe Wald - University of Illinois at Chicago
Allen D Seftel - University of Illinois at Chicago
Lawrence S Ross - University of Illinois at Chicago
Mamdouh A B Mohamed - University of Illinois at Chicago
Craig S Niederberger - University of Illinois at Chicago
Resource Type: Journal article
Publication Details: The Journal of urology, Vol.173(1), pp.167-170
DOI: 10.1097/01.ju.0000146839.43427.0b
PMID: 15592067
ISSN: 0022-5347
eISSN: 1527-3792
Grant note: P01 HD 36289 / NICHD NIH HHS
Language: English
Date published: 01/2005
Academic Unit: Urology
Record Identifier: 9984320854802771

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