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Comparative Analysis of Machine Learning Algorithms in Diagnosis of Polycystic Ovarian Syndrome

by Malik Mubasher Hassan, Tabasum Mirza
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 175 - Number 17
Year of Publication: 2020
Authors: Malik Mubasher Hassan, Tabasum Mirza
10.5120/ijca2020920688

Malik Mubasher Hassan, Tabasum Mirza . Comparative Analysis of Machine Learning Algorithms in Diagnosis of Polycystic Ovarian Syndrome. International Journal of Computer Applications. 175, 17 ( Sep 2020), 42-53. DOI=10.5120/ijca2020920688

@article{ 10.5120/ijca2020920688,
author = { Malik Mubasher Hassan, Tabasum Mirza },
title = { Comparative Analysis of Machine Learning Algorithms in Diagnosis of Polycystic Ovarian Syndrome },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2020 },
volume = { 175 },
number = { 17 },
month = { Sep },
year = { 2020 },
issn = { 0975-8887 },
pages = { 42-53 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume175/number17/31548-2020920688/ },
doi = { 10.5120/ijca2020920688 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:25:20.172572+05:30
%A Malik Mubasher Hassan
%A Tabasum Mirza
%T Comparative Analysis of Machine Learning Algorithms in Diagnosis of Polycystic Ovarian Syndrome
%J International Journal of Computer Applications
%@ 0975-8887
%V 175
%N 17
%P 42-53
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Artificial intelligence can be used in healthcare systems for diagnostic purposes to handle large amounts of clinical data with much accuracy and precision. One of the commonest health issue found in the young women is Polycystic Ovarian Syndrome (PCOS) and it is basically a complex health disorder affecting women of reproductive age group that can be diagnosed based on clinical symptoms like increased body mass index, elevated hormone levels, hair loss, acne, skin darkening, hirustism, cycle length, endometrial thickness, high blood pressure levels, etc. Correct diagnosis is the baseline of any proper treatment and in this research paper we are using machine learning approaches like Support Vector Machine, CART, Naive Bayes Classification, Random Forest and Logistic Regression to diagnose PCOS based on the clinical data of patients. The results were analyzed and performance of the algorithms was validated on the basis of accuracy, precision, recall, F-statistics, and Kappa Coefficient. The validation metrics indicate the highest i.e. 96% accuracy of the Random Forest algorithm in the diagnosis of PCOS on giving data.

References
  1. G. N. Allahbadia and R. Merchant, “Polycystic ovary syndrome and impact on health,” Middle East Fertil. Soc. J., vol. 16, no. 1, pp. 19–37, 2011, doi: 10.1016/j.mefs.2010.10.002.
  2. G. P. Rédei, “Polycystic Ovarian Disease (Stein-Leventhal syndrome),” in Encyclopedia of Genetics, Genomics, Proteomics and Informatics, Springer Netherlands, 2008, pp. 1528–1528.
  3. I. F. Stein and M. L. Leventhal, “Amenorrhea associated with bilateral polycystic ovaries,” Am. J. Obstet. Gynecol., vol. 29, no. 2, pp. 181–191, 1935, doi: 10.1016/s0002-9378(15)30642-6.
  4. R. Rebar, Evaluation of Amenorrhea, Anovulation, and Abnormal Bleeding. MDText.com, Inc., 2000.
  5. S. M. Sirmans and K. A. Pate, “Epidemiology, diagnosis, and management of polycystic ovary syndrome,” Clin. Epidemiol., vol. 6, no. 1, pp. 1–13, 2013, doi: 10.2147/clep.s37559.
  6. R. Azziz et al., The Androgen Excess and PCOS Society criteria for the polycystic ovary syndrome: the complete task force report, vol. 91, no. 2. 2009.
  7. W. U. Atiomo, S. Pearson, S. Shaw, A. Prentice, and P. Dubbins, “Ultrasound criteria in the diagnosis of polycystic ovary syndrome (PCOS),” Ultrasound Med. Biol., vol. 26, no. 6, pp. 977–980, Jul. 2000, doi: 10.1016/S0301-5629(00)00219-2.
  8. A. H. Balen et al., “Andrology: Polycystic ovary syndrome: The spectrum of the disorder in 1741 patients,” Hum. Reprod., vol. 10, no. 8, pp. 2107–2111, 1995, doi: 10.1093/oxfordjournals.humrep.a136243.
  9. B. Y. Jarrett et al., “Impact of right–left differences in ovarian morphology on the ultrasound diagnosis of polycystic ovary syndrome,” Fertil. Steril., vol. 112, no. 5, pp. 939–946, Nov. 2019, doi: 10.1016/j.fertnstert.2019.06.016.
  10. T. Williams, “Diagnosis and Treatment of Polycystic Ovary Syndrome,” Jul. 2016. Accessed: Jul. 25, 2020. [Online]. Available: http://familydoctor.org/familydoctor/en/diseases-conditions/polycystic-ovary-syndrome.html.
  11. D. A. Ehrmann et al., “Prevalence and predictors of the metabolic syndrome in women with polycystic ovary syndrome,” J. Clin. Endocrinol. Metab., vol. 91, no. 1, pp. 48–53, Jan. 2006, doi: 10.1210/jc.2005-1329.
  12. S. Sam, “Obesity and polycystic ovary syndrome,” Obesity Management, vol. 3, no. 2. pp. 69–73, Apr. 2007, doi: 10.1089/obe.2007.0019.
  13. M. P. Lauritsen, P. F. Svendsen, and A. N. Andersen, “Diagnostic criteria for polycystic ovary syndrome,” Ugeskr. Laeger, vol. 181, no. 15, pp. 671–679, 2019, doi: 10.1016/S1701-2163(16)32915-2.Diagnostic.
  14. M. J. Himelein and S. S. Thatcher, “Depression and body image among women with polycystic ovary syndrome,” J. Health Psychol., vol. 11, no. 4, pp. 613–625, Jul. 2006, doi: 10.1177/1359105306065021.
  15. A. Dunaif, K. R. Segal, W. Futterweit, and A. Dobrjansky, “Profound peripheral insulin resistance, independent of obesity, in polycystic ovary syndrome,” Diabetes, vol. 38, no. 9, pp. 1165–1174, 1989, doi: 10.2337/diab.38.9.1165.
  16. R. S. Legro, “Polycystic Ovary Syndrome and Cardiovascular Disease: A Premature Association?,” Endocr. Rev., vol. 24, no. 3, pp. 302–312, Jun. 2003, doi: 10.1210/er.2003-0004.
  17. L. M. Liao, J. Nesic, P. M. Chadwick, K. Brooke-Wavell, and G. M. Prelevic, “Exercise and body image distress in overweight and obese women with polycystic ovary syndrome: A pilot investigation,” Gynecol. Endocrinol., vol. 24, no. 10, pp. 555–561, 2008, doi: 10.1080/09513590802288226.
  18. R. A. Wild, “Dyslipidemia in PCOS,” in Steroids, Mar. 2012, vol. 77, no. 4, pp. 295–299, doi: 10.1016/j.steroids.2011.12.002.
  19. E. C. Costa et al., “Aerobic Training Improves Quality of Life in Women with Polycystic Ovary Syndrome,” Med. Sci. Sports Exerc., vol. 50, no. 7, pp. 1357–1366, Jul. 2018, doi: 10.1249/MSS.0000000000001579.
  20. M. A. Karimzadeh and M. Javedani, “An assessment of lifestyle modification versus medical treatment with clomiphene citrate, metformin, and clomiphene citrate-metformin in patients with polycystic ovary syndrome,” Fertil. Steril., vol. 94, no. 1, pp. 216–220, Jun. 2010, doi: 10.1016/j.fertnstert.2009.02.078.
  21. I. Almenning, A. Rieber-Mohn, K. M. Lundgren, T. S. Løvvik, K. K. Garnæs, and T. Moholdt, “Effects of high intensity interval training and strength training on metabolic, cardiovascular and hormonal outcomes in women with polycystic ovary syndrome: A pilot study,” PLoS One, vol. 10, no. 9, Sep. 2015, doi: 10.1371/journal.pone.0138793.
  22. D. R. Chizen et al., “The ‘pulse’ diet & PCOS,” Fertil. Steril., vol. 102, no. 3, p. e267, Sep. 2014, doi: 10.1016/j.fertnstert.2014.07.908.
  23. D. S. Kiddy et al., “Improvement in endocrine and ovarian function during dietary treatment of obese women with polycystic ovary syndrome,” Clin. Endocrinol. (Oxf)., vol. 36, no. 1, pp. 105–111, 1992, doi: 10.1111/j.1365-2265.1992.tb02909.x.
  24. H. H. Mehrabani, S. Salehpour, B. J. Meyer, and F. Tahbaz, “Beneficial effects of a high-protein, low-glycemic-load hypocaloric diet in overweight and obese women with polycystic ovary syndrome: A randomized controlled intervention study,” J. Am. Coll. Nutr., vol. 31, no. 2, pp. 117–125, Apr. 2012, doi: 10.1080/07315724.2012.10720017.
  25. F. Giallauria et al., “Exercise training improves autonomic function and inflammatory pattern in women with polycystic ovary syndrome (PCOS),” Clin. Endocrinol. (Oxf)., vol. 69, no. 5, pp. 792–798, Nov. 2008, doi: 10.1111/j.1365-2265.2008.03305.x.
  26. U. A. Ndefo, A. Eaton, and M. R. Green, “Polycystic ovary syndrome: A review of treatment options with a focus on pharmacological approaches,” P T, vol. 38, no. 6, pp. 336–355, Jun. 2013, Accessed: Jul. 25, 2020. [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3737989/.
  27. L. Radosh, “Drug Treatments for Polycystic Ovary Syndrome,” Apr. 2009. Accessed: Jul. 25, 2020. [Online]. Available: www.aafp.org/afp.
  28. G. Ladson et al., “The effects of metformin with lifestyle therapy in polycystic ovary syndrome: A randomized double-blind study,” Fertil. Steril., vol. 95, no. 3, Mar. 2011, doi: 10.1016/j.fertnstert.2010.12.002.
  29. A. Gambineri et al., “Treatment with flutamide, metformin, and their combination added to a hypocaloric diet in overweight-obese women with polycystic ovary syndrome: A randomized, 12-month, placebo-controlled study,” J. Clin. Endocrinol. Metab., vol. 91, no. 10, pp. 3970–3980, 2006, doi: 10.1210/jc.2005-2250.
  30. V. Deepika, “Applications of Artificial Intelligence Techniques in Polycystic ovarian syndrome Diagnosis,” J. Adv. Res. Technol. Manag. Sci., vol. 1, no. 3, pp. 59–63, 2019, doi: 10.1007/978-981-13-2640-0.
  31. A. Saravanan and S. Sathiamoorthy, “Detection of Polycystic Ovarian Syndrome : A Literature Survey,” vol. 7, no. 2, pp. 46–51, 2018.
  32. V. Krishnaveni, “A Roadmap to a Clinical Prediction Model With Computational Intelligence for PCOS,” Vol. Ix, No. Ii, Pp. 177–185.
Index Terms

Computer Science
Information Sciences

Keywords

Polycystic Ovarian Syndrome Machine Learning Diagnosis and Random Forest

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