Predicting Seminal Quality via Imbalanced Learning with Evolutionary Safe-Level Synthetic Minority Over-Sampling Technique

Seminal quality has fallen dramatically over the past two decades. Research indicates that environmental factors, health status, and life habits might lead to the decline. Prediction of seminal quality is very useful in the early diagnosis of infertile patients. Recently, artificial intelligence (AI) technologies have been applied to the study of the male fertility potential. As it is common in many real applications about cognitive computation, seminal quality prediction faces the problem of class imbalance, and conventional algorithms are often biased towards the majority class. In this paper, an evolutionary safe-level synthetic minority over-sampling technique (ESLSMOTE) is proposed to synthesize the minority instances along the same line with different weight degree, called safe level. The profile of seminal of an individual from the fertility dataset is predicted via three classification methods with ESLSMOTE. Important indicators, such as accuracy, precision, recall, receiver operating characteristic (ROC) curve, and F1-score, are used to evaluate the performance of the classifiers with ESLSMOTE based on a tenfold cross-validation scheme. The experimental results show that the proposed ESLSMOTE can significantly improve the accuracy of back-propagation neural network, adaptive boosting, and support vector machine. The highest area under the ROC curve (97.2%) is given by the ESLSMOTE-AdaBoost model. Experimental results indicate that the ESLSMOTE-based classifiers outperform current state-of-the-art methods on predicting the seminal quality in terms of the accuracy and the area under the ROC curve. As such, the ESLSMOTE-based classifiers have the capability of predicting the seminal quality with high accuracy.