Seminal Quality Prediction Using Deep Learning Based on Artificial Intelligence
Abstract
Fertility rates have dramatically decreased in
the last two decades, especially in men. It has been described that
environmental factors, as well as life habits, may affect semen quality. This
paper evaluates the performance of different artificial intelligence (AI)
techniques for classifying fertility dataset that includes the semen sample
analysed according to WHO 2010 criteria and publicly available on UCI data
repository. In this context, deep
neural network (DNN) which involved in many studies in recent years is proposed
to classify fertility dataset successfully. For the purpose of comparing the
proposed method’s performance, Adaptive Neuro-Fuzzy Inference system (ANFIS) is
also used for the classification problem. The results show that the performance
of the DNN has the best with the average accuracy rate of 90.11%, and the
results of the other ANFIS methods are also satisfactory.
Keywords
Classification,fertility,statistical methods,artificial intelligence,deep learning,ANFIS
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