Shiraz E-Medical Journal

Published by: Kowsar

Factors Affecting Endometriosis in Women of Reproductive Age: The Differences Between the Results of Neural Network and Logistic Regression

Shahla Chaichian 1 , Jamileh Abolghasemi 2 , Fatemeh Naji Omidi 3 , Shahnaz Rimaz 4 , * , Zahra Najmi 5 , Abolfazl Mehdizadehkashi 6 and Bahram Moazzami 7
Authors Information
1 Minimally Invasive Techniques Research Center in Women, Tehran Medical Sciences Branch, Islamic Azad University, Tehran, Iran
2 Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
3 School of Public Health, Iran University of Medical Sciences, Tehran, Iran
4 Department of Epidemiology, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
5 Zanjan University of Medical Sciences, Zanjan, Iran
6 Endometriosis Research Center, Iran University of Medical Sciences, Tehran, Iran
7 Pars Advanced and Minimally Invasive Medical Manners Research Center, Pars Hospital, Iran University of Medical Sciences, Tehran, Iran
Article information
  • Shiraz E-Medical Journal: September 2018, 19 (9); e62560
  • Published Online: August 5, 2018
  • Article Type: Research Article
  • Received: October 7, 2017
  • Revised: June 18, 2018
  • Accepted: June 19, 2018
  • DOI: 10.5812/semj.62560

To Cite: Chaichian S, Abolghasemi J, Naji Omidi F , Rimaz S, Najmi Z, et al. Factors Affecting Endometriosis in Women of Reproductive Age: The Differences Between the Results of Neural Network and Logistic Regression, Shiraz E-Med J. 2018 ; 19(9):e62560. doi: 10.5812/semj.62560.

Abstract
Copyright © 2018, Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited
1. Background
2. Methods
3. Results
4. Discussion
Acknowledgements
Footnotes
References
  • 1. Somigliana E, Infantino M, Benedetti F, Arnoldi M, Calanna G, Ragni G. The presence of ovarian endometriomas is associated with a reduced responsiveness to gonadotropins. Fertil Steril. 2006;86(1):192-6. doi: 10.1016/j.fertnstert.2005.12.034. [PubMed: 16716316].
  • 2. Abeshouse BS, Abeshouse G. Endometriosis of the urinary tract: a review of the literature and a report of four cases of vesical endometriosis. J Int Coll Surg. 1960;34:43-63. [PubMed: 13791480].
  • 3. Vigano P, Parazzini F, Somigliana E, Vercellini P. Endometriosis: epidemiology and aetiological factors. Best Pract Res Clin Obstet Gynaecol. 2004;18(2):177-200. doi: 10.1016/j.bpobgyn.2004.01.007. [PubMed: 15157637].
  • 4. Cramer DW, Missmer SA. The epidemiology of endometriosis. Ann N Y Acad Sci. 2002;955:11-22. discussion 34-6, 396-406. doi: 10.1111/j.1749-6632.2002.tb02761.x. [PubMed: 11949940].
  • 5. Eskenazi B, Warner ML. Epidemiology of endometriosis. Obstet Gynecol Clin North Am. 1997;24(2):235-58. doi: 10.1016/S0889-8545(05)70302-8. [PubMed: 9163765].
  • 6. Harrison RF, Kennedy RL. Artificial neural network models for prediction of acute coronary syndromes using clinical data from the time of presentation. Ann Emerg Med. 2005;46(5):431-9. doi: 10.1016/j.annemergmed.2004.09.012. [PubMed: 16271675].
  • 7. Moini A, Malekzadeh F, Amirchaghmaghi E, Kashfi F, Akhoond MR, Saei M, et al. Risk factors associated with endometriosis among infertile Iranian women. Arch Med Sci. 2013;9(3):506-14. doi: 10.5114/aoms.2013.35420. [PubMed: 23847674]. [PubMed Central: PMC3701984].
  • 8. Chaichian S, Mehdizadehkashi A, Najmi Z, Mobasseri A, Jahanloo A, Mohabbatian B, et al. Clinical predictive factors for diagnosis of endometriosis in iranian infertile population. J Minim Invasive Surg Sci. 2015;4(3). doi: 10.17795/minsurgery-24236.
  • 9. Mood C. Logistic regression: Why we cannot do what we think we can do, and what we can do about it. Eur Sociol Rev. 2009;26(1):67-82. doi: 10.1093/esr/jcp006.
  • 10. Lapuerta P, L'Italien GJ, Paul S, Hendel RC, Leppo JA, Fleisher LA, et al. Neural network assessment of perioperative cardiac risk in vascular surgery patients. Med Decis Making. 1998;18(1):70-5. doi: 10.1177/0272989X9801800114. [PubMed: 9456211].
  • 11. Veltri RW, Chaudhari M, Miller MC, Poole EC, O'Dowd GJ, Partin AW. Comparison of logistic regression and neural net modeling for prediction of prostate cancer pathologic stage. Clin Chem. 2002;48(10):1828-34. [PubMed: 12324513].
  • 12. Dayhoff JE. Neural network architectures: an introduction. Van Nostrand Reinhold Co; 1990.
  • 13. Nevill AM, Atkinson G, Hughes MD, Cooper SM. Statistical methods for analysing discrete and categorical data recorded in performance analysis. J Sports Sci. 2002;20(10):829-44. doi: 10.1080/026404102320675666. [PubMed: 12363298].
  • 14. Agresti A, Kateri M. Categorical Data Analysis. Springer; 2011. doi: 10.1007/978-3-642-04898-2_161.
  • 15. Matalliotakis IM, Cakmak H, Fragouli YG, Goumenou AG, Mahutte NG, Arici A. Epidemiological characteristics in women with and without endometriosis in the Yale series. Arch Gynecol Obstet. 2008;277(5):389-93. doi: 10.1007/s00404-007-0479-1. [PubMed: 17922285].
  • 16. Collazo MS, Porrata-Doria T, Flores I, Acevedo SF. Apolipoprotein E polymorphisms and spontaneous pregnancy loss in patients with endometriosis. Mol Hum Reprod. 2012;18(7):372-7. doi: 10.1093/molehr/gas004. [PubMed: 22266326]. [PubMed Central: PMC3378308].
  • 17. Burghaus S, Klingsiek P, Fasching PA, Engel A, Haberle L, Strissel PL, et al. Risk Factors for Endometriosis in a German Case-Control Study. Geburtshilfe Frauenheilkd. 2011;71(12):1073-9. doi: 10.1055/s-0031-1280436. [PubMed: 25253901]. [PubMed Central: PMC4166917].
  • 18. Hemmings R, Rivard M, Olive DL, Poliquin-Fleury J, Gagne D, Hugo P, et al. Evaluation of risk factors associated with endometriosis. Fertil Steril. 2004;81(6):1513-21. doi: 10.1016/j.fertnstert.2003.10.038. [PubMed: 15193470].
  • 19. Kennedy S. Who gets endometriosis? Women Health Med. 2005;2(1):18-9. doi: 10.1383/wohm.2.1.18.58876.
  • 20. Kirshon B, Poindexter AN 3rd. Contraception: a risk factor for endometriosis. Obstet Gynecol. 1988;71(6 Pt 1):829-31. [PubMed: 3368167].
  • 21. Ballweg ML. Impact of endometriosis on women's health: comparative historical data show that the earlier the onset, the more severe the disease. Best Pract Res Clin Obstet Gynaecol. 2004;18(2):201-18. doi: 10.1016/j.bpobgyn.2004.01.003. [PubMed: 15157638].
  • 22. Lemaire GS. More than just menstrual cramps: symptoms and uncertainty among women with endometriosis. J Obstet Gynecol Neonatal Nurs. 2004;33(1):71-9. doi: 10.1177/0884217503261085. [PubMed: 14971555].
  • 23. Hardiman P, Pillay OC, Atiomo W. Polycystic ovary syndrome and endometrial carcinoma. Lancet. 2003;361(9371):1810-2. doi: 10.1016/S0140-6736(03)13409-5. [PubMed: 12781553].
  • 24. Siristatidis CS, Chrelias C, Pouliakis A, Katsimanis E, Kassanos D. Artificial neural networks in gynaecological diseases: current and potential future applications. Med Sci Monit. 2010;16(10):RA231-6. [PubMed: 20885366].
  • 25. Karakitsos P, Kyroudes A, Pouliakis A, Stergiou EB, Voulgaris Z, Kittas C. Potential of the learning vector quantizer in the cell classification of endometrial lesions in postmenopausal women. Anal Quant Cytol Histol. 2002;24(1):30-8. [PubMed: 11865947].
  • 26. Ture M, Kurt I, Turhankurum A, Ozdamar K. Comparing classification techniques for predicting essential hypertension. Expert Syst Appl. 2005;29(3):583-8. doi: 10.1016/j.eswa.2005.04.014.
  • 27. Mohamed EI, Linder R, Perriello G, Di Daniele N, Poppl SJ, De Lorenzo A. Predicting Type 2 diabetes using an electronic nose-based artificial neural network analysis. Diabetes Nutr Metab. 2002;15(4):215-21. [PubMed: 12416658].
  • 28. Kurt I, Ture M, Kurum AT. Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease. Expert Syst Appl. 2008;34(1):366-74. doi: 10.1016/j.eswa.2006.09.004.
  • 29. Lin CC, Bai YM, Chen JY, Hwang TJ, Chen TT, Chiu HW, et al. Easy and low-cost identification of metabolic syndrome in patients treated with second-generation antipsychotics: artificial neural network and logistic regression models. J Clin Psychiatry. 2010;71(3):225-34. doi: 10.4088/JCP.08m04628yel. [PubMed: 19814949].
  • 30. Eller-Vainicher C, Zhukouskaya VV, Tolkachev YV, Koritko SS, Cairoli E, Grossi E, et al. Low bone mineral density and its predictors in type 1 diabetic patients evaluated by the classic statistics and artificial neural network analysis. Diabetes Care. 2011;34(10):2186-91. doi: 10.2337/dc11-0764. [PubMed: 21852680]. [PubMed Central: PMC3177712].
  • 31. Chien CW, Lee YC, Ma T, Lee TS, Lin YC, Wang W, et al. The application of artificial neural networks and decision tree model in predicting post-operative complication for gastric cancer patients. Hepatogastroenterology. 2008;55(84):1140-5. [PubMed: 18705347].
  • 32. Karakitsos P, Pouliakis A, Kordalis G, Georgoulakis J, Kittas C, Kyroudes A. Potential of radial basis function neural networks in discriminating benign from malignant lesions of the lower urinary tract. Anal Quant Cytol Histol. 2005;27(1):35-42. [PubMed: 15794450].
  • 33. Jaimes F, Farbiarz J, Alvarez D, Martinez C. Comparison between logistic regression and neural networks to predict death in patients with suspected sepsis in the emergency room. Crit Care. 2005;9(2):R150-6. doi: 10.1186/cc3054. [PubMed: 15774048]. [PubMed Central: PMC1175932].
Creative Commons License Except where otherwise noted, this work is licensed under Creative Commons Attribution Non Commercial 4.0 International License .

Search Relations:

Author(s):

Article(s):

Create Citiation Alert
via Google Reader

Readers' Comments