A Predictive Model for Seminal Quality Using Neutrosophic Rule-Based Classification System

This paper presents a predictive model of sperm quality based on personal lifestyle and environmental factors. Predicting the fertility based on measuring the quality of sperm is an urgent research problem since the fertility rates have been decreased mostly in men. Infertility is the biggest problem which faces the married persons. A lot of tests are expensive and time-consuming. Hence, these tests are not suitable for evaluating the quality of sperm; as a consequence, there is a need for implementing computational models that can predict the quality of sperm. The proposed model consists of two stages. In the first stage, Neutrosophic Rule-based Classification System (NRCS) and genetic NRCS (GNRCS) are proposed to classify an unknown seminal quality into normal or abnormal. NRCS and GNRCS forecast fertility based on nine input parameters which cover person lifestyle. The NRCS and GNRCS models were compared with three well-known classifiers such as decision trees, Support Vector Machines and Multilayer Perceptron with quality measures of GM, sensitivity, specificity, and accuracy. The proposed model (GNRCS) achieved promising results of 98.03% accuracy while basic NRCS achieved 95.55% accuracy. In the second stage, three different sampling algorithms are used to obtain balanced data.