Design of a Clinical Decision Support System for Predicting Erectile Dysfunction in Men Using NHIRD Dataset.

Erectile dysfunction (ED) affects millions of men worldwide. 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 age 70. A variety of chronic diseases, including diabetes, ischemic heart disease, congestive heart failure, hypertension, depression, chronic renal failure, obstructive sleep apnea, prostate disease, gout, and sleep disorder, were reported to be associated with ED. In this study, data retrieved from a subset of the National Health Insurance Research Database (NHIRD) of Taiwan were used for designing the clinical decision support system (CDSS) for predicting ED incidences in men. The positive cases were male patients aged 20-65 who were diagnosed with ED between Jan. 2000 and Dec. 2010 confirmed by at least 3 outpatient visits or at least one inpatient visit, while the negative cases were randomly selected from the database without a history of ED and were frequency (1:1), age, and index year matched with the ED patients. Data of a total of 2,832 ED patients and 2,832 non-ED patients, each consisting of 41 features including index age, 10 comorbidities, and 30 other comorbidity-related variables, were retrieved for designing the predictive models. Integrated genetic algorithm (GA) and support vector machine (SVM) was adopted to design the CDSSs with 2 experiments of independent training and testing (ITT) conducted to verify their effectiveness. In the 1st ITT experiment, data extracted from Jan. 2000 till Dec. 2005 (61.51%, 1,742 positive cases and 1,742 negative cases) were used for training and validating and the data retrieved from Jan. 2006 till Dec. 2010 were used for testing (38.49%); whereas in the 2nd ITT experiment, data in the training set (77.78%) were extracted from Jan. 2000 till Dec. 2007 and those in the testing set (22.22%) were retrieved afterward. Tenfold cross validation and 3 different objective functions were adopted for obtaining the optimal models with best predictive performance in the training phase. The testing results show that the CDSSs achieved a predictive performance with accuracy, sensitivity, specificity, g-mean, and area under ROC curve (AUC) of 74.72-76.65%, 72.33-83.76%, 69.54-77.10%, 0.7468-0.7632, and 0.766-0.817, respectively. In conclusion, the CDSSs designed based on cost-sensitive objective functions as well as salient comorbidity-related features achieve satisfactory predictive performance for predicting ED incidences.