Öz
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.
Anahtar Kelimeler
Classification fertility statistical methods artificial intelligence deep learning ANFIS
Kaynakça
- Bidgoli, A.A., Komleh, H.E., & Mousavirad, S.J., (2015). Seminal quality prediction using optimized artificial neural network with genetic algorithm. 9th International Conference on Electrical and Electronics Engineering (ELECO), 695-699.
- Gil, D., Girela, J. L., De Juan, J., Gomez-Torres, M. J., & Johnsson, M., (2012). Predicting seminal quality with artificial intelligence methods. Expert Systems with Applications. 39, 12564-12573.
- Gil, D., & Girela, J.L., (accessed on 8 November 2013). UCI Machine Learning Repository: Fertility data set. Available online: http://archive.ics.uci.edu/ml/datasets/Fertility.
- Girela, J. L., Gil, D., Johnsson, M., Gomez-Torres, M. J., & De Juan, J., (2013). Semen parameters can be predicted from environmental factors and lifestyle using artificial intelligence methods. Biology of Reproduction, 88, 99.
- LeCun, Y., Bengio, Y., & Hinton, G., (2015). Deep learning. Nature. 521 (7553), 436–444.
- Lu, J., Huang, Y., & LA, N., (2010). [WHO laboratory manual for the examination and processing of human semen: Its applicability to andrology laboratories in China],” Zhonghua nan ke xue= National journal of andrology. 16 (10), 867- 871.
- Tieleman, T., & Hinton, G., (2012). Lecture 6.5-RMSprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning, 4.
- Wang, H., Xu, Q., & Zhou, L., (2014). Seminal quality prediction using clustering-based decision forests. Algorithms. 7, 405-417.
Öz
Kaynakça
- Bidgoli, A.A., Komleh, H.E., & Mousavirad, S.J., (2015). Seminal quality prediction using optimized artificial neural network with genetic algorithm. 9th International Conference on Electrical and Electronics Engineering (ELECO), 695-699.
- Gil, D., Girela, J. L., De Juan, J., Gomez-Torres, M. J., & Johnsson, M., (2012). Predicting seminal quality with artificial intelligence methods. Expert Systems with Applications. 39, 12564-12573.
- Gil, D., & Girela, J.L., (accessed on 8 November 2013). UCI Machine Learning Repository: Fertility data set. Available online: http://archive.ics.uci.edu/ml/datasets/Fertility.
- Girela, J. L., Gil, D., Johnsson, M., Gomez-Torres, M. J., & De Juan, J., (2013). Semen parameters can be predicted from environmental factors and lifestyle using artificial intelligence methods. Biology of Reproduction, 88, 99.
- LeCun, Y., Bengio, Y., & Hinton, G., (2015). Deep learning. Nature. 521 (7553), 436–444.
- Lu, J., Huang, Y., & LA, N., (2010). [WHO laboratory manual for the examination and processing of human semen: Its applicability to andrology laboratories in China],” Zhonghua nan ke xue= National journal of andrology. 16 (10), 867- 871.
- Tieleman, T., & Hinton, G., (2012). Lecture 6.5-RMSprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning, 4.
- Wang, H., Xu, Q., & Zhou, L., (2014). Seminal quality prediction using clustering-based decision forests. Algorithms. 7, 405-417.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 31 Ocak 2019 |
| Gönderilme Tarihi | 19 Kasım 2018 |
| Yayımlandığı Sayı | Yıl 2019 Cilt: 11 Sayı: 1 |