Semen quality predictive model using Feed Forwarded Neural Network trained by Learning-Based Artificial Algae Algorithm

Abstract

Recent scientific studies have noted that the seminal quality of males is significantly decreasing due to lifestyle and environmental factors. Clinical diagnosis of sperm quality is one important aspect of identifying the potential of semen for the occurrence of pregnancy. Due to the advances in machine learning algorithms, especially the reliable and high classification accuracy of neural network in health related problems, it is becoming possible to predict seminal quality from lifestyle data. In this respect, a few attempts were made in predicting seminal quality. These studies were conducted using imbalanced data sets, where the performance outcomes tend to be biased towards the majority class. Other studies implemented the gradient descent technique for training the neural network. The gradient descent is a local training technique that is prone to get stuck to local minima. On the contrary, meta-heuristic algorithms enable searching solutions both locally and globally. Therefore, in this study, Artificial Algae Algorithm that is improved using a Learning-Based fitness evaluation method is proposed for training Feed Forward Neural Network (FFNN). In addition, the SMOTE data balancing method was employed to balance normal and abnormal instances. Experimental analyses were carried out to evaluate the predictive accuracy of the FFNN trained using Learning-Based Artificial Algae Algorithm (FFNN-LBAAA). The results were compared with well-known machine learning algorithms, namely: Multi-layer Perceptron Neural Network (MLP), Naive Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest (RF) algorithms. The proposed approach showed superior performance in discriminating normal and abnormal semen quality instances over the other compared algorithms. (C) 2020 Karabuk University. Publishing services by Elsevier B.V.