The application of machine learning techniques to the prediction of erectile dysfunction

Erectile dysfunction (ED) is a multifactorial disorder that can cause significant distress for men. Risk factor identification may allow for future ED prevention or delay onset. The goal of this investigation is, 1) to evaluate different machine learning approaches for prognosticating ED and, 2) to analyze the degree of importance of ED risk factors. The investigated machine learning approaches include: 1) logistic regression as a statistical method, 2) multilayer feedforward backpropagation neural networks (an artificial neural-network tool), 3) the fuzzy K-nearest neighbor classifier as a fuzzy logic method; 4) support vector machine (SVM), a relatively new machine learning process, and 5) conventional discriminant function analysis. The overall results obtained indicate that the artificial neural network method yields the highest ROC-AUC, and that it has produced the most reliable model for prognosticating ED when compared to the other investigated models.