Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016)

Decision Support Systems for Predicting Erectile Dysfunction

Authors
Hsuan-Hung LIN, Po-Chou CHAN, Ming-Huei LEE, Yung-Fu CHEN, Yung-Kuan CHAN, Wei-Sheng CHUNG, Dah-Jye LEE
Corresponding Author
Hsuan-Hung LIN
Available Online December 2016.
DOI
https://doi.org/10.2991/cnct-16.2017.83How to use a DOI?
Keywords
Erectile Dysfunction, Chronic Disease, Decision Support System
Abstract

Men with ED generally complain failure to attain or maintain an adequate erection during sexual activity. The prevalence of ED is strongly correlated with age, affecting about 40% of men at age 40 and nearly 70% at aged 70. A variety of chronic diseases, including diabetes, hypertension, cardiovascular disease (CVD), chronic renal failure, depression, sleep disorder, and gout were shown to be associated with ED. In this study, the data used for designing the clinical decision support system (CDSS) was retrieved from a subset of the National Health Insurance Research Database (NHIRD) of Taiwan. The positive cases were male patients with age ranging from 20 to 65 years old who were diagnosed with ED (ICD-9-CM Code 607.84) between Jan. 2000 and Dec. 2010 confirmed by at least 3 outpatient visits, while the negative cases were randomly selected from the database with no history of ED and 1:1 frequency- and age-matched with the ED patients. Experiments of 1 cross validation (10 folds) and 2 independent training and testing (ITT) were conducted to verify the effectiveness of the predictive models. The results show that the sensitivity, specificity, and accuracy of tenfold cross validation achieved 69.45%, 69.45%, and 69.54%, respectively. For the ITT experiments, the sensitivity, specificity, accuracy, and the area under ROC curve were 70.28%, 72.48%, 71.32%, and 0.7226, respectively in the first experiment, and 69.42%, 70.74%, 70.06%, and 0.7143, respectively, in the second experiment. Future works will focus on designing the CDSSs with ensemble classifiers consisting of multiple SVM models by adopting the laboratory data to improve the predictive performance for ED prediction.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016)
Series
Advances in Computer Science Research
Publication Date
December 2016
ISBN
978-94-6252-301-2
ISSN
2352-538X
DOI
https://doi.org/10.2991/cnct-16.2017.83How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Hsuan-Hung LIN
AU  - Po-Chou CHAN
AU  - Ming-Huei LEE
AU  - Yung-Fu CHEN
AU  - Yung-Kuan CHAN
AU  - Wei-Sheng CHUNG
AU  - Dah-Jye LEE
PY  - 2016/12
DA  - 2016/12
TI  - Decision Support Systems for Predicting Erectile Dysfunction
BT  - Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016)
PB  - Atlantis Press
SP  - 606
EP  - 612
SN  - 2352-538X
UR  - https://doi.org/10.2991/cnct-16.2017.83
DO  - https://doi.org/10.2991/cnct-16.2017.83
ID  - LIN2016/12
ER  -