Skip to main content
SpringerLink
Log in

Detection of Male Fertility Using AI-Driven Tools

  • Conference paper
  • First Online:
Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1576))

Abstract

In the last few decades, the nation has been experiencing a low fertility rate due to fast changes in human lifestyle over a short period. Many lifestyle factors, such as liquor consumption, physical latency, cigarette smoking, caffeine intake, and others, can adversely affect on reproductive performance. These factors are associated with sperm quality, which is a pivotal key feature to identify male fertility status. In this experiment, three different feature selection methods have been applied to assess the uppermost features which are deeply connected with seminal quality. The final dataset contains three lifestyle features of hundred males under 18 to 36 years of age, having normal and altered output labels. Four artificial intelligence methods such as logistics regression, support vector machine, decision tree, and k-nearest neighbor are utilized to identify the male reproductive state. Finally, K-nearest neighbor algorithm has excelled in male fertility prognosis with 90% efficacy, and the receiver operating characteristic value is 0.85.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bidgoli, A.A., Komleh, H.E., Mousavirad, S.J.: Seminal quality prediction using optimized artificial neural network with genetic algorithm. In: 2015 9th International Conference on Electrical and Electronics Engineering (ELECO), pp. 695–699. IEEE (2015)

    Google Scholar 

  2. Candemir, C.: Estimating the semen quality from life-style using fuzzy radial basis functions. Int. J. Mach. Learn. Comput. 8(1), 44–8 (2018)

    Article  Google Scholar 

  3. Dash, S.R., Ray, R.: Predicting seminal quality and its dependence on life style factors through ensemble learning. Int. J. E-Health Med. Commun. (IJEHMC) 11(2), 78–95 (2020)

    Article  Google Scholar 

  4. Dey, N., Ashour, A.S., Borra, S.: Classification in BioApps: Automation of Decision Making, vol. 26. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-65981-7

  5. Durairajanayagam, D.: Lifestyle causes of male infertility. Arab. J. Urol. 16(1), 10–20 (2018)

    Article  Google Scholar 

  6. Engy, E., Ali, E., Sally, E.G.: An optimized artificial neural network approach based on sperm whale optimization algorithm for predicting fertility quality. Stud Inf. Control 27(3), 349–358 (2018)

    Article  Google Scholar 

  7. Girela, J.L., Gil, D., Johnsson, M., Gomez-Torres, M.J., De Juan, J.: Semen parameters can be predicted from environmental factors and lifestyle using artificial intelligence methods. Biol. Reprod. 88(4), 99–1 (2013)

    Article  Google Scholar 

  8. Hamet, P., Tremblay, J.: Artificial intelligence in medicine. Metabolism 69, S36–S40 (2017)

    Article  Google Scholar 

  9. Kumar, S., Murarka, S., Mishra, V., Gautam, A.: Environmental and lifestyle factors in deterioration of male reproductive health. Indian J. Med. Res. 140(Suppl 1), S29 (2014)

    Google Scholar 

  10. Kurt, I., Ture, M., Kurum, A.T.: Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease. Expert Syst. Appl. 34(1), 366–374 (2008)

    Article  Google Scholar 

  11. Mathur, P.P., D’cruz, S.C.: The effect of environmental contaminants on testicular function. Asian J. Androl. 13(4), 585 (2011)

    Article  Google Scholar 

  12. Mendoza-Palechor, F.E., Ariza-Colpas, P.P., Sepulveda-Ojeda, J.A., De-la Hoz-Manotas, A., Piñeres Melo, M.: Fertility analysis method based on supervised and unsupervised data mining techniques. Int. J. Appl. Eng. 11, 10374–10379 (2016)

    Google Scholar 

  13. Nath, S.S., Mishra, G., Kar, J., Chakraborty, S., Dey, N.: A survey of image classification methods and techniques. In: 2014 International conference on control, instrumentation, communication and computational technologies (ICCICCT), pp. 554–557. IEEE (2014)

    Google Scholar 

  14. Podgorelec, V., Kokol, P., Stiglic, B., Rozman, I.: Decision trees: an overview and their use in medicine. J. Med. Syst. 26(5), 445–463 (2002). https://doi.org/10.1023/A:1016409317640

    Article  Google Scholar 

  15. Rhemimet, A., Raghay, S., Bencharef, O.: Comparative analysis of classification, clustering and regression techniques to explore men’s fertility. In: El Oualkadi, A., Choubani, F., El Moussati, A. (eds.) Proceedings of the Mediterranean Conference on Information and Communication Technologies 2015. LNEE, vol. 380, pp. 455–462. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-30301-7_48

    Chapter  Google Scholar 

  16. Riordon, J., McCallum, C., Sinton, D.: Deep learning for the classification of human sperm. Comput. Biol. Med. 111, 103342 (2019)

    Article  Google Scholar 

  17. Sahoo, A.J., Kumar, Y.: Seminal quality prediction using data mining methods. Technol. Health Care 22(4), 531–545 (2014)

    Article  Google Scholar 

  18. Sharma, K., Virmani, J.: A decision support system for classification of normal and medical renal disease using ultrasound images: a decision support system for medical renal diseases. Int. J. Ambient Comput. Intell. (IJACI) 8(2), 52–69 (2017)

    Article  Google Scholar 

  19. Sharpe, R.M.: Environmental/lifestyle effects on spermatogenesis. Philos. Trans. R. Soc. B: Biol. Sci. 365(1546), 1697–1712 (2010)

    Article  Google Scholar 

  20. Simfukwe, M., Kunda, D., Chembe, C.: Comparing Naive Bayes method and artificial neural network for semen quality categorization. Int. J. Innovative Sci. Eng. Technol. 2(7), 689–694 (2015)

    Google Scholar 

  21. Soltanzadeh, S., Zarandi, M.H.F., Astanjin, M.B.: A hybrid fuzzy clustering approach for fertile and unfertile analysis. In: 2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS), pp. 1–6. IEEE (2016)

    Google Scholar 

  22. UCI: Fertility data set. https://archive.ics.uci.edu/ml/datasets/Fertility. Accessed 11 Feb 2021

  23. Virtanen, H., Rajpert-De Meyts, E., Main, K., Skakkebaek, N., Toppari, J.: Testicular dysgenesis syndrome and the development and occurrence of male reproductive disorders. Toxicol. Appl. Pharmacol. 207(2), 501–505 (2005)

    Article  Google Scholar 

  24. Wang, Y., et al.: Morphological segmentation analysis and texture-based support vector machines classification on mice liver fibrosis microscopic images. Curr. Bioinform. 14(4), 282–294 (2019)

    Article  Google Scholar 

  25. Zemmal, N., Azizi, N., Dey, N., Sellami, M.: Adaptive semi supervised support vector machine semi supervised learning with features cooperation for breast cancer classification. J. Med. Imaging Health Inf. 6(1), 53–62 (2016)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Automation, Banasthali Vidyapith, Banasthali, 304022, Rajasthan, India

    Debasmita Ghosh Roy

  2. Department of Physics, Banasthali Vidyapith, Banasthali, 304022, Rajasthan, India

    P. A. Alvi

Authors
  1. Debasmita Ghosh Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. A. Alvi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Debasmita Ghosh Roy .

Editor information

Editors and Affiliations

  1. University of South Dakota, Vermillion, SD, USA

    KC Santosh

  2. Central University of Karnataka, Gulbarga, India

    Ravindra Hegadi

  3. Indian Statistical Institute, Kolkata, India

    Umapada Pal

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Roy, D.G., Alvi, P.A. (2022). Detection of Male Fertility Using AI-Driven Tools. In: Santosh, K., Hegadi, R., Pal, U. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2021. Communications in Computer and Information Science, vol 1576. Springer, Cham. https://doi.org/10.1007/978-3-031-07005-1_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-07005-1_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-07004-4

  • Online ISBN: 978-3-031-07005-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation