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.