Skip to main content

Ensemble Learning for Data-Driven Diagnosis of Polycystic Ovary Syndrome

  • Conference paper
  • First Online:
Intelligent Systems Design and Applications (ISDA 2021)

Abstract

The emphasis of this article is on the data-driven diagnosis of polycystic ovary syndrome (PCOS) in women. Data from the Kaggle repository is used to train ensemble machine learning algorithms. There are 177 women with PCOS in this dataset, which includes 43 different characteristics. To begin, a univariate feature selection and feature elimination method are used to identify the most accurate characteristics for predicting PCOS. The characteristics are ranked, and the ratio of Follicle-stimulating hormone (FSH) to Luteinizing hormone (LH) is determined to be the most significant one. Cross-validation method is applied while the feature selection and feature elimination are occurring. Voting hard, voting soft and CatBoost are among the classifiers used on the dataset. According to the findings, the top 13 most significant risk factors accurately predict the onset of PCOS. With the use of 5, 10, 20-fold cross-validation on ensemble learning’s 13 most critical characteristics, results show that soft voting has the highest accuracy of 91.12%. As a result, ensemble learning can be used to accurately identify PCOS patients.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
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. Hiremath, P.S., Tegnoor, J.R.: Follicle detection and ovarian classification in digital ultrasound images of ovaries. Adv. Breakthroughs Ultrasound Imag. 5, 167–199 (2013)

    Google Scholar 

  2. Balen, A.H., Laven, J.S.E., Tan, S.L., Dewailly, D.: Ultrasound assessment of the polycystic ovary: international consensus definitions. Hum. Reprod. Update 9(6), 505–514 (2003)

    Article  Google Scholar 

  3. Pache, T.D., Wladimiroff, J.W., Hop, W.C., Fauser, B.C.: How to discriminate between normal and polycystic ovaries: transvaginal US study. Radiology 183(2), 421–423 (1992)

    Article  Google Scholar 

  4. Kelsey, T.W., Wallace, W.H.B.: Ovarian volume correlates strongly with the number of nongrowing follicles in the human ovary. In: Obstetrics and Gynecology International 2012 (2012)

    Google Scholar 

  5. Priya, N., Jeevitha, S.: Overview of an ovarian classification and detection PCOS in ultrasound image: a study. In: Jain, L.C., Peng, S.-L., Alhadidi, B., Pal, S. (eds.) ICICCT 2019. LAIS, vol. 9, pp. 359–365. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-38501-9_36

    Chapter  Google Scholar 

  6. Obermayer-Pietsch, B., Lerchbaum, E.: Journal für Klinische Endokrinologie und Stoffwechsel 12(4), 170–173 (2019). https://doi.org/10.1007/s41969-019-00084-7

    Article  Google Scholar 

  7. Dumesic, D.A., Oberfield, S.E., Stener-Victorin, E., Marshall, J.C., Laven, J.S., Legro, R.S.: Scientific statement on the diagnostic criteria, epidemiology, pathophysiology, and molecular genetics of polycystic ovary syndrome. Endocr. Rev. 36(5), 487–525 (2015)

    Article  Google Scholar 

  8. Cheng, J.J., Mahalingaiah, S.: Data mining and classification of polycystic ovaries in pelvic ultrasound reports. bioRxiv:254870 (2018)

    Google Scholar 

  9. Padmapriya, B., Kesavamurthy, T.: Detection of follicles in poly cystic ovarian syndrome in ultrasound images using morphological operations. J. Med. Imag. Health Inf. 6(1), 240–243 (2016)

    Google Scholar 

  10. Gibson-Helm, M., Teede, H., Dunaif, A., Dokras, A.: Delayed diagnosis and a lack of information associated with dissatisfaction in women with polycystic ovary syndrome. J. Clin. Endocrinol. Metab. 102(2), 604–612 (2017)

    Google Scholar 

  11. Mehrotra, P., Chatterjee, J., Chakraborty, C., Ghoshdastidar, B., Ghoshdastidar, S.: Automated screening of polycystic ovary syndrome using machine learning techniques. In: Paper Presented at the 2011 Annual IEEE India Conference, 2011 (2011)

    Google Scholar 

  12. Bharati, S., Podder, P., Mondal, M.R.H.: Diagnosis of Polycystic Ovary Syndrome Using Machine Learning Algorithms. In: 2020 IEEE, pp 1486–1489 (2020)

    Google Scholar 

  13. Mahmood, N.H., Ahmmad, S.N.Z., Hashim, H., Rani, S.: Ovary ultrasound image edge detection analysis: a tutorial using MATLAB. Int. J. Eng. Res. Appl. 2(3), 1635–1642 (2012)

    Google Scholar 

  14. Vasavi, G., Jyothi, S.: Classification and detection of ovarian cysts in ultrasound Images. In: Paper presented at the 2017 International Conference on Trends in Electronics and Informatics (ICEI), 2017 (2017)

    Google Scholar 

  15. Padmapriya, B., Kesavamurthy, T.: Diagnostic tool for PCOS classification. In: Goh, J., Lim, C.T. (eds.) 7th WACBE World Congress on Bioengineering 2015. IP, vol. 52, pp. 182–185. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19452-3_48

    Chapter  Google Scholar 

  16. Brower, M., Brennan, K., Pall, M., Azziz, R.: The severity of menstrual dysfunction as a predictor of insulin resistance in PCOS. J. Clin. Endocrinol. Metab. 98(12), E1967–E1971 (2013)

    Article  Google Scholar 

  17. Dewailly, D., et al.: Definition and significance of polycystic ovarian morphology: a task force report from the Androgen Excess and Polycystic Ovary Syndrome Society. Hum. Reprod. Update 20(3), 334–352 (2014)

    Article  Google Scholar 

  18. Raj, A.: Detection of cysts in ultrasonic images of ovary. Int. J. Sci. Res. (IJSR) 2(8), 185–189 (2013)

    Google Scholar 

  19. Dewi, R.M., Adiwijaya, U.N., Wisesty, J.: Classification of polycystic ovary based on ultrasound images using competitive neural network. J. Phys. Conf. Ser. 971, 012005 (2018). https://doi.org/10.1088/1742-6596/971/1/012005

    Article  Google Scholar 

  20. Bharati, S., Podder, P., Mondal, M.R.H.: Artificial neural network based breast cancer screening: a comprehensive review. Int. J. Comput. Inf. Syst. Ind. Manage. Appl. 12, 125–137 (2020)

    Google Scholar 

  21. Bharati, S., Podder, P., Mondal, M., Prasath, V.B.: CO-ResNet: optimized ResNet model for COVID-19 diagnosis from X-ray images. Int. J. Hybrid Intell. Syst. 17, 71–85 (2021). https://doi.org/10.3233/HIS-210008

    Article  Google Scholar 

  22. Bharati, S., Podder, P., Mondal, M., Prasath, V.B.: Medical imaging with deep learning for COVID-19 diagnosis: a comprehensive review. Int. J. Comput. Inf. Syst. Ind. Manage. Appl. 13, 91–112 (2021)

    Google Scholar 

  23. Bharati, S., Prajoy Podder, M., Mondal, R.H., Gandhi, N.: Optimized NASNet for diagnosis of COVID-19 from lung CT images. In: Abraham, A., Piuri, V., Gandhi, N., Siarry, P., Kaklauskas, A., Madureira, A. (eds.) ISDA 2020. AISC, vol. 1351, pp. 647–656. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-71187-0_59

    Chapter  Google Scholar 

  24. Bharati, S., Prajoy Podder, M., Mondal, R.H.: Hybrid deep learning for detecting lung diseases from X-ray images. Inf. Med. Unlocked 20, 100391 (2020)

    Article  Google Scholar 

  25. Sachdeva, G., Gainder, S., Suri, V., Sachdeva, N., Chopra, S.: Obese and non-obese polycystic ovarian syndrome: comparison of clinical, metabolic, hormonal parameters, and their differential response to clomiphene. Indian J. Endocrinol. Metabol. 23(2), 257 (2019)

    Article  Google Scholar 

  26. Mondal, M.R.H., Bharati, S., Podder, P.: CO-IRv2: Optimized InceptionResNetV2 for COVID-19 detection from chest CT images. PLoS ONE 16(10), e0259179 (2021)

    Article  Google Scholar 

  27. Zhang, X.-Z., Pang, Y.-L., Wang, X., Li, Y.-H.: Computational characterization and identification of human polycystic ovary syndrome genes. Sci. Rep. 8(1), 1–7 (2018)

    Google Scholar 

  28. Denny, A., Raj, A., Ashok, A., Ram, C.M.: George R I-HOPE: detection and prediction system for polycystic ovary syndrome (PCOS) using machine learning techniques. In: TENCON 2019–2019 IEEE Region 10 Conference (TENCON), 2019, pp 673–678. IEEE (2019)

    Google Scholar 

  29. Joham, A.E., Teede, H.J., Ranasinha, S., Zoungas, S., Boyle, J.: Prevalence of infertility and use of fertility treatment in women with polycystic ovary syndrome: data from a large community-based cohort study. J. Womens Health 24(4), 299–307 (2015)

    Article  Google Scholar 

  30. Kottarathil, P.: Polycystic ovary syndrome (PCOS). https://www.kaggle.com/prasoonkottarathil/polycystic-ovary-syndrome-pcos. Accessed 18 Nov 2021

  31. Podder, P., Khamparia, A., Mondal, M.R.H., Rahman, M.A., Bharati, S.: Forecasting the Spread of COVID-19 and ICU Requirements. Int. J, Online Biomed. Eng. (iJOE) 5, 81–99 (2021)

    Google Scholar 

  32. Mondal, M.R.H., Bharati, S., Podder, P.: Diagnosis of COVID-19 using machine learning and deep learning: a review. In: Current Medical Imaging (2021)

    Google Scholar 

  33. Raihan-Al-Masud, M., Mondal, M.R.H.: Data-driven diagnosis of spinal abnormalities using feature selection and machine learning algorithms. PLoS ONE 15(2), e0228422 (2020)

    Article  Google Scholar 

  34. Podder, P., Subrato Bharati, M., Mondal, R.H., Kose, U.: Application of machine learning for the diagnosis of COVID-19. In: Data Science for COVID-19, pp. 175–194. Elsevier (2021). https://doi.org/10.1016/B978-0-12-824536-1.00008-3

    Chapter  Google Scholar 

  35. Podder, P., Subrato Bharati, M., Mondal, R.H.: 10 Automated gastric cancer detection and classification using machine learning. In: Gupta, D., Kose, U., Le Nguyen, B., Bhattacharyya, S. (eds.) Artificial Intelligence for Data-Driven Medical Diagnosis, pp. 207–224. De Gruyter (2021). https://doi.org/10.1515/9783110668322-010

    Chapter  Google Scholar 

  36. Mondal, M.R.H., Bharati, S., Podder, P., Podder, P.: Data analytics for novel coronavirus disease. Inf. Med. Unlocked 20, 100374 (2020)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Subrato Bharati .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bharati, S., Podder, P., Mondal, M.R.H., Surya Prasath, V.B., Gandhi, N. (2022). Ensemble Learning for Data-Driven Diagnosis of Polycystic Ovary Syndrome. In: Abraham, A., Gandhi, N., Hanne, T., Hong, TP., Nogueira Rios, T., Ding, W. (eds) Intelligent Systems Design and Applications. ISDA 2021. Lecture Notes in Networks and Systems, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-96308-8_116

Download citation

Publish with us

Policies and ethics