Predicting Seminal Quality Using Back-Propagation Neural Networks with Optimal Feature Subsets

Jieming Ma*, Aiyan Zhen, Sheng Uei Guan, Chun Liu, Xin Huang

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

4 Citations (Scopus)


Many studies have shown that there is a decline in seminal quality during the past two decades. Seminal quality may be affected by environmental factors and health status, as well as life habits. Artificial intelligence (AI) technology has been recently applied to recognize this effect. However, conventional AI algorithms are not prepared to cope with the class-imbalanced fertility dataset. To this end, a back-propagation neural network (BPNN) is used to predict the seminal profile of an individual from the dataset. A neural-genetic algorithm (N-GA) is employed to select optimal feature subsets and optimize the parameters of the used neural network. Results indicate that the proposed method outperforms other AI methods on seminal quality prediction in terms of precision and accuracy.

Original languageEnglish
Title of host publicationAdvances in Brain Inspired Cognitive Systems - 9th International Conference, BICS 2018, Proceedings
EditorsCheng-Lin Liu, Jinchang Ren, Amir Hussain, Bin Luo, Huimin Zhao, Jiangbin Zheng, Xinbo Zhao
PublisherSpringer Verlag
Number of pages9
ISBN (Print)9783030005627
Publication statusPublished - 2018
Event9th International Conference on Brain-Inspired Cognitive Systems, BICS 2018 - Xi'an, China
Duration: 7 Jul 20188 Jul 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10989 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference9th International Conference on Brain-Inspired Cognitive Systems, BICS 2018

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