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Gebelikte demir eksikliği anemisinin kural tabanlı akıllı sınıflandırma modelleri kullanılarak incelenmesi

Year 2023, Volume: 8 Issue: 6, 154 - 164, 28.12.2023

Mehmet Onur Kaya Rüveyda Yıldırım Burkay Yakar Bilal Alatas

https://doi.org/10.22391/fppc.1347373

Abstract

Giriş: Demir eksikliği anemisi, dünya çapında aneminin en yaygın nedenidir ve hamilelik sırasında artan demir gereksinimi anemi riskini artırır. Gebelikte anemi, düşük doğum ağırlığı, preterm ve intrauterin gelişme geriliği gibi olumsuz gebelik sonuçları ile ilişkilidir. Bu çalışma, gebelik sırasında demir eksikliği anemisi üzerindeki sosyo-demografik, beslenme, antenatal bakım ve obstetrik faktörleri tahmin etmek için Kural Tabanlı Akıllı Sınıflandırma Modelleri kullanmıştır.
Yöntem: Bu retrospektif çalışma, Türkiye'nin doğusundaki Elazığ ilinde Ocak ve Haziran 2019 tarihleri arasında yürütülen toplum temelli kesitsel bir çalışmanın ikincil bir analiziydi. Çalışmaya 495 gebenin verileri dahil edildi. Demir eksikliği anemisi hemoglobin  < 11,0 g/dl ve ferritin <30,0 µg/L olarak tanımlandı. Hamilelik sırasında anemi ile ilişkili faktörleri tahmin etmek için kural tabanlı makine öğrenimi yöntemleri kullanıldı.
Bulgular: 495 gebenin yaş ortalaması 30,06 ± 5,15 yıldı. Çalışma popülasyonunda anemi prevalansı %27,9 idi. Anne yaşı, eğitim durumu, meslek, beslenme eğitimi durumu, beslenme özelliği, gravida ve parite anemi ile anlamlı şekilde ilişkiliydi. Jrip, OneR ve PART algoritmaları anemi ile ilişkili faktörleri sırasıyla %96,36, %85,45 ve %97,98 doğrulukla tahmin etti.
Sonuç: Kural tabanlı makine öğrenimi algoritması, hamilelik sırasında demir eksikliği anemisi için risk faktörlerine yeni bir yaklaşım sunabilir. Bu model ile gebelik öncesi ve gebelik anında anemi riski tahmin edilebilir ve önleyici girişimler yapılabilir.

Keywords

Gebelik Demir eksikliği anemisi Algoritmalar Makine öğrenme

Ethical Statement

The current study protocol was approved by Firat University's non-interventional research ethics committee (date: 08.04.2021, IRB number: 2021/05/05). Written consent of the participants was not required as it was a retrospective study. Necessary permissions were obtained from the corresponding author for the reanalysis of the data of the study entitled "Prevalence of Anemia and Associated Risk Factors among Pregnant Women, What is the Role of Antenatal Care in Prevention? A Cross-sectional Study".

Thanks

We thank all authors for allowing us to use the data of the study "Prevalence of Anemia and Associated Risk Factors among Pregnant Women, What is the Role of Antenatal Care in Prevention? A Cross-sectional Study".

References

  • 1. James AH. Iron deficiency anemia in pregnancy. Obstet Gynecol. 2021;138(4):663-74. https://doi.org/10.1097/aog.0000000000004559
  • 2. WHO Global anaemia estimates, 2021 Edition. Access date: 27.07.2022 available at: https://www.who.int/data/gho/data/themes/topics/anaemia_in_women_and_children#:~:text=Summary%20findings&text=In%202019%2C%20global%20anaemia%20prevalence,women%20aged%2015%2D49%20years. (Access Date: December 22, 2023)
  • 3. Ullah A, Sohaib M, Saeed F, Iqbal S. Prevalence of anemia and associated risk factors among pregnant women in Lahore, Pakistan. Women Health 2019;59(6):660-71. https://doi.org/10.1080/03630242.2018.1544966
  • 4. Yakar B, Pirincci E, Kaya MO, Onalan E. Prevalence of anemia and associated risk factors among pregnant women, what is the role of antenatal care in prevention? A cross-sectional study. J Coll Physicians Surg Pak. 2021;31(11):1341-45. https://doi.org/10.29271/jcpsp.2021.11.1341
  • 5. Mahamoud NK, Mwambi B, Oyet C, Segujja F, Webbo F, Okiria JC, et al. Prevalence of anemia and its associated socio-demographic factors among pregnant women attending an antenatal care clinic at Kisugu health center IV, Makindye division, Kampala, Uganda. J Blood Med 2020;11:13-8. https://doi.org/10.2147/jbm.s231262
  • 6. World Health Organization. Meeting to develop a global consensus on preconception care to reduce maternal and childhood mortality and morbidity; 6-7 February 2012 Meeting Report; World Health Organization: Geneva, Switzerland, 2013. Available at: https://apps.who.int/iris/handle/10665/78067 (Access Date: December 22, 2023)
  • 7. Deo RC. Machine Learning in Medicine. Circulation. 2015;132(20):1920-30. https://doi.org/10.1161/circulationaha.115.001593
  • 8. Handelman GS, Kok HK, Chandra RV, Razavi AH, Lee MJ, Asadi H. eDoctor: machine learning and the future of medicine. J Intern Med. 2018;284(6):603-19. https://doi.org/10.1111/joim.12822
  • 9. Cohen WW. Fast effective rule induction, twelfth international conference on machine learning. Morgan Kaufmann 1995:115-23.
  • 10. Akyol S, Alatas B. Automatic mining of accurate and comprehensible numerical classification rules with cat swarm optimization algorithm. J Fac Eng Arch Gazi Uni 2016;31:839-57.
  • 11. Holte, RC. Very simple classification rules perform well on most commonly used datasets. Machine Learning 1993;1(1):63-90.
  • 12. Frank E, Witten IH. Generating accurate rule sets without global optimization. Machine Learning: Proceedings of the fifteenth international conference, Madison, Wisconsin, 1998:144-51.
  • 13. Balaban M, Kartal E. Data mining and machine learning basic algorithms and applications with R programming. 2sd Ed. İstanbul: Caglayan Publishing House, 2018.
  • 14. Lebso M, Anato A, Loha E. Prevalence of anemia and associated factors among pregnant women in Southern Ethiopia: A community based cross-sectional study. PLoS One. 2017;12(12):e0188783. https://doi.org/10.1371/journal.pone.0188783
  • 15. Wu Y, Ye H, Liu J, Ma Q, Yuan Y, Pang Q, et al. Prevalence of anemia and sociodemographic characteristics among pregnant and non-pregnant women in southwest China: a longitudinal observational study. BMC Pregnancy Childbirth. 2020;20(1):535. https://doi.org/10.1186/s12884-020-03222-1
  • 16. Gupta PM, Hamner HC, Suchdev PS, Flores-Ayala R, Mei Z. Iron status of toddlers, nonpregnant females, and pregnant females in the United States. Am J Clin Nutr. 2017;106(Suppl 6):1640-46. https://doi.org/10.3945/ajcn.117.155978
  • 17. Addis Alene K, Mohamed Dohe A. Prevalence of anemia and associated factors among pregnant women in an urban area of eastern Ethiopia. Anemia. 2014;2014:561567. https://doi.org/10.1155/2014/561567
  • 18. Viveki RG, Halappanavar AB, Vivek PR, Halki SB, Maled VS, Deshpande PS. Prevalence of anemia and its epidemiological determinants in pregnant women. Al Ameen J Med Sci. 2012;5(3):216–23.
  • 19. Kangalgil M, Sahinler A, Kirkbir IB, Ozcelik AO. Associations of maternal characteristics and dietary factors with anemia and iron-deficiency in pregnancy. J Gynecol Obstet Hum Reprod. 2021;50(8):102137. https://doi.org/10.1016/j.jogoh.2021.102137
  • 20. Zerfu TA, Umeta M, Baye K. Dietary diversity during pregnancy is associated with reduced risk of maternal anemia, preterm delivery, and low birth weight in a prospective cohort study in rural Ethiopia. Am J Clin Nutr. 2016;103(6):1482-88. https://doi.org/10.3945/ajcn.115.116798
  • 21. Chakrabarti S, George N, Majumder M, Raykar N, Scott S. Identifying sociodemographic, programmatic and dietary drivers of anaemia reduction in pregnant Indian women over 10 years. Public Health Nutr. 2018;21(13):2424-33. https://doi.org/10.1017/s1368980018000903
  • 22. Lazrak M, El Kari K, Stoffel NU, Elammari L, Al-Jawaldeh A, Loechl CU, et al. Tea consumption reduces iron bioavailability from NaFeEDTA in nonanemic women and women with iron deficiency anemia: Stable iron isotope studies in Morocco. J Nutr. 2021; 151(9):2714-20. https://doi.org/10.1093/jn/nxab159
  • 23. Machmud P, Hatma R, Syafiq A. Tea consumption and iron-deficiency anemia among pregnant woman in Bogor district, Indonesia. Media Gizi Mikro Indonesia 2019;10(2):91-100. https://doi.org/10.22435/mgmi.v10i2.1384
  • 24. Zijp IM, Korver O, Tijburg LB. Effect of tea and other dietary factors on iron absorption. Crit Rev Food Sci Nutr. 2000;40(5):371-98. https://doi.org/10.1080/10408690091189194
  • 25. Ugwu NI, Uneke CJ. Iron deficiency anemia in pregnancy in Nigeria-A systematic review. Niger J Clin Pract. 2020;23(7):889-96. https://doi.org/10.4103/njcp.njcp_197_19
  • 26. Sow B, Mukhtar H, Ahmad HF, Suguri H. Assessing the relative importance of social determinants of health in malaria and anemia classification based on machine learning techniques. Inform Health Soc Care. 2020;45(3):229-41. https://doi.org/10.1080/17538157.2019.1582056
  • 27. Dauvin A, Donado C, Bachtiger P, Huang KC, Sauer CM, Ramazzotti D, et al. Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients. NPJ Digit Med. 2019;2:116. https://doi.org/10.1038/s41746-019-0192-z
  • 28. Li Y, Chen M, Lv H, Yin P, Zhang L, Tang P. A novel machine-learning algorithm for predicting mortality risk after hip fracture surgery. Injury. 2021;52(6):1487-93. https://doi.org/10.1016/j.injury.2020.12.008

Analyzing of iron-deficiency anemia in pregnancy using rule-based intelligent classification models

Year 2023, Volume: 8 Issue: 6, 154 - 164, 28.12.2023

Mehmet Onur Kaya Rüveyda Yıldırım Burkay Yakar Bilal Alatas

https://doi.org/10.22391/fppc.1347373

Abstract

Introduction: Iron deficiency anemia is the most common cause of anemia worldwide, and increased iron requirement during pregnancy increases the risk of anemia. Anemia in pregnancy is associated with adverse pregnancy outcomes such as low birth weight, preterm and intrauterine growth restriction. This study used a Rule-based Intelligent Classification Models to predict socio-demographic, nutritional, antenatal care and obstetric factors on iron deficiency anemia during pregnancy
Methods: This retrospective study was a secondary analysis of a community-based cross-sectional study conducted between January and June 2019 in the province of Elazig in eastern Turkey. Data of 495 pregnant women were included in the study iron deficiency anemia was defined as hemoglobin  < 11 g/dl, and ferritin < 30 µg/L. Rule-based machine learning methods were used to predict factors associated with anemia during pregnancy.
Results: The mean age of 495 pregnant women were 30.06 ± 5.15 years. The prevalence of anemia was 27.9% in study population. Maternal age, educational status, occupation, nutrition education status, nutritional property, gravida, and parity were significantly related to anemia. Jrip, OneR, and PART algorithms estimated factors associated with anemia with 96.36%, 85.45%, and 97.98% accuracy, respectively.
Conclusion: Rule-based machine learning algorithm may offer a new approach to risk factors for iron deficiency anemia during pregnancy. With the use of this model, it is possible to predict the risk of anemia both before and during pregnancy and to take preventative measures.

Keywords

Pregnancy Iron-Deficiency Anemia Algorithms Machine learning

Ethical Statement

The current study protocol was approved by Firat University's non-interventional research ethics committee (date: 08.04.2021, IRB number: 2021/05/05). Written consent of the participants was not required as it was a retrospective study. Necessary permissions were obtained from the corresponding author for the reanalysis of the data of the study entitled "Prevalence of Anemia and Associated Risk Factors among Pregnant Women, What is the Role of Antenatal Care in Prevention? A Cross-sectional Study".

Thanks

We thank all authors for allowing us to use the data of the study "Prevalence of Anemia and Associated Risk Factors among Pregnant Women, What is the Role of Antenatal Care in Prevention? A Cross-sectional Study".

References

  • 1. James AH. Iron deficiency anemia in pregnancy. Obstet Gynecol. 2021;138(4):663-74. https://doi.org/10.1097/aog.0000000000004559
  • 2. WHO Global anaemia estimates, 2021 Edition. Access date: 27.07.2022 available at: https://www.who.int/data/gho/data/themes/topics/anaemia_in_women_and_children#:~:text=Summary%20findings&text=In%202019%2C%20global%20anaemia%20prevalence,women%20aged%2015%2D49%20years. (Access Date: December 22, 2023)
  • 3. Ullah A, Sohaib M, Saeed F, Iqbal S. Prevalence of anemia and associated risk factors among pregnant women in Lahore, Pakistan. Women Health 2019;59(6):660-71. https://doi.org/10.1080/03630242.2018.1544966
  • 4. Yakar B, Pirincci E, Kaya MO, Onalan E. Prevalence of anemia and associated risk factors among pregnant women, what is the role of antenatal care in prevention? A cross-sectional study. J Coll Physicians Surg Pak. 2021;31(11):1341-45. https://doi.org/10.29271/jcpsp.2021.11.1341
  • 5. Mahamoud NK, Mwambi B, Oyet C, Segujja F, Webbo F, Okiria JC, et al. Prevalence of anemia and its associated socio-demographic factors among pregnant women attending an antenatal care clinic at Kisugu health center IV, Makindye division, Kampala, Uganda. J Blood Med 2020;11:13-8. https://doi.org/10.2147/jbm.s231262
  • 6. World Health Organization. Meeting to develop a global consensus on preconception care to reduce maternal and childhood mortality and morbidity; 6-7 February 2012 Meeting Report; World Health Organization: Geneva, Switzerland, 2013. Available at: https://apps.who.int/iris/handle/10665/78067 (Access Date: December 22, 2023)
  • 7. Deo RC. Machine Learning in Medicine. Circulation. 2015;132(20):1920-30. https://doi.org/10.1161/circulationaha.115.001593
  • 8. Handelman GS, Kok HK, Chandra RV, Razavi AH, Lee MJ, Asadi H. eDoctor: machine learning and the future of medicine. J Intern Med. 2018;284(6):603-19. https://doi.org/10.1111/joim.12822
  • 9. Cohen WW. Fast effective rule induction, twelfth international conference on machine learning. Morgan Kaufmann 1995:115-23.
  • 10. Akyol S, Alatas B. Automatic mining of accurate and comprehensible numerical classification rules with cat swarm optimization algorithm. J Fac Eng Arch Gazi Uni 2016;31:839-57.
  • 11. Holte, RC. Very simple classification rules perform well on most commonly used datasets. Machine Learning 1993;1(1):63-90.
  • 12. Frank E, Witten IH. Generating accurate rule sets without global optimization. Machine Learning: Proceedings of the fifteenth international conference, Madison, Wisconsin, 1998:144-51.
  • 13. Balaban M, Kartal E. Data mining and machine learning basic algorithms and applications with R programming. 2sd Ed. İstanbul: Caglayan Publishing House, 2018.
  • 14. Lebso M, Anato A, Loha E. Prevalence of anemia and associated factors among pregnant women in Southern Ethiopia: A community based cross-sectional study. PLoS One. 2017;12(12):e0188783. https://doi.org/10.1371/journal.pone.0188783
  • 15. Wu Y, Ye H, Liu J, Ma Q, Yuan Y, Pang Q, et al. Prevalence of anemia and sociodemographic characteristics among pregnant and non-pregnant women in southwest China: a longitudinal observational study. BMC Pregnancy Childbirth. 2020;20(1):535. https://doi.org/10.1186/s12884-020-03222-1
  • 16. Gupta PM, Hamner HC, Suchdev PS, Flores-Ayala R, Mei Z. Iron status of toddlers, nonpregnant females, and pregnant females in the United States. Am J Clin Nutr. 2017;106(Suppl 6):1640-46. https://doi.org/10.3945/ajcn.117.155978
  • 17. Addis Alene K, Mohamed Dohe A. Prevalence of anemia and associated factors among pregnant women in an urban area of eastern Ethiopia. Anemia. 2014;2014:561567. https://doi.org/10.1155/2014/561567
  • 18. Viveki RG, Halappanavar AB, Vivek PR, Halki SB, Maled VS, Deshpande PS. Prevalence of anemia and its epidemiological determinants in pregnant women. Al Ameen J Med Sci. 2012;5(3):216–23.
  • 19. Kangalgil M, Sahinler A, Kirkbir IB, Ozcelik AO. Associations of maternal characteristics and dietary factors with anemia and iron-deficiency in pregnancy. J Gynecol Obstet Hum Reprod. 2021;50(8):102137. https://doi.org/10.1016/j.jogoh.2021.102137
  • 20. Zerfu TA, Umeta M, Baye K. Dietary diversity during pregnancy is associated with reduced risk of maternal anemia, preterm delivery, and low birth weight in a prospective cohort study in rural Ethiopia. Am J Clin Nutr. 2016;103(6):1482-88. https://doi.org/10.3945/ajcn.115.116798
  • 21. Chakrabarti S, George N, Majumder M, Raykar N, Scott S. Identifying sociodemographic, programmatic and dietary drivers of anaemia reduction in pregnant Indian women over 10 years. Public Health Nutr. 2018;21(13):2424-33. https://doi.org/10.1017/s1368980018000903
  • 22. Lazrak M, El Kari K, Stoffel NU, Elammari L, Al-Jawaldeh A, Loechl CU, et al. Tea consumption reduces iron bioavailability from NaFeEDTA in nonanemic women and women with iron deficiency anemia: Stable iron isotope studies in Morocco. J Nutr. 2021; 151(9):2714-20. https://doi.org/10.1093/jn/nxab159
  • 23. Machmud P, Hatma R, Syafiq A. Tea consumption and iron-deficiency anemia among pregnant woman in Bogor district, Indonesia. Media Gizi Mikro Indonesia 2019;10(2):91-100. https://doi.org/10.22435/mgmi.v10i2.1384
  • 24. Zijp IM, Korver O, Tijburg LB. Effect of tea and other dietary factors on iron absorption. Crit Rev Food Sci Nutr. 2000;40(5):371-98. https://doi.org/10.1080/10408690091189194
  • 25. Ugwu NI, Uneke CJ. Iron deficiency anemia in pregnancy in Nigeria-A systematic review. Niger J Clin Pract. 2020;23(7):889-96. https://doi.org/10.4103/njcp.njcp_197_19
  • 26. Sow B, Mukhtar H, Ahmad HF, Suguri H. Assessing the relative importance of social determinants of health in malaria and anemia classification based on machine learning techniques. Inform Health Soc Care. 2020;45(3):229-41. https://doi.org/10.1080/17538157.2019.1582056
  • 27. Dauvin A, Donado C, Bachtiger P, Huang KC, Sauer CM, Ramazzotti D, et al. Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients. NPJ Digit Med. 2019;2:116. https://doi.org/10.1038/s41746-019-0192-z
  • 28. Li Y, Chen M, Lv H, Yin P, Zhang L, Tang P. A novel machine-learning algorithm for predicting mortality risk after hip fracture surgery. Injury. 2021;52(6):1487-93. https://doi.org/10.1016/j.injury.2020.12.008
There are 28 citations in total.

Details

Primary Language English
Subjects Family Medicine
Journal Section Original Research
Authors

Mehmet Onur Kaya 0000-0001-8052-0484

Rüveyda Yıldırım 0000-0002-6675-2622

Burkay Yakar 0000-0003-2745-6561

Bilal Alatas 0000-0002-3513-0329

Publication Date December 28, 2023
Submission Date August 22, 2023
Acceptance Date December 13, 2023
Published in Issue Year 2023Volume: 8 Issue: 6

Cite

APA Kaya, M. O., Yıldırım, R., Yakar, B., Alatas, B. (2023). Analyzing of iron-deficiency anemia in pregnancy using rule-based intelligent classification models. Family Practice and Palliative Care, 8(6), 154-164. https://doi.org/10.22391/fppc.1347373
AMA Kaya MO, Yıldırım R, Yakar B, Alatas B. Analyzing of iron-deficiency anemia in pregnancy using rule-based intelligent classification models. Fam Pract Palliat Care. December 2023;8(6):154-164. doi:10.22391/fppc.1347373
Chicago Kaya, Mehmet Onur, Rüveyda Yıldırım, Burkay Yakar, and Bilal Alatas. “Analyzing of Iron-Deficiency Anemia in Pregnancy Using Rule-Based Intelligent Classification Models”. Family Practice and Palliative Care 8, no. 6 (December 2023): 154-64. https://doi.org/10.22391/fppc.1347373.
EndNote Kaya MO, Yıldırım R, Yakar B, Alatas B (December 1, 2023) Analyzing of iron-deficiency anemia in pregnancy using rule-based intelligent classification models. Family Practice and Palliative Care 8 6 154–164.
IEEE M. O. Kaya, R. Yıldırım, B. Yakar, and B. Alatas, “Analyzing of iron-deficiency anemia in pregnancy using rule-based intelligent classification models”, Fam Pract Palliat Care, vol. 8, no. 6, pp. 154–164, 2023, doi: 10.22391/fppc.1347373.
ISNAD Kaya, Mehmet Onur et al. “Analyzing of Iron-Deficiency Anemia in Pregnancy Using Rule-Based Intelligent Classification Models”. Family Practice and Palliative Care 8/6 (December 2023), 154-164. https://doi.org/10.22391/fppc.1347373.
JAMA Kaya MO, Yıldırım R, Yakar B, Alatas B. Analyzing of iron-deficiency anemia in pregnancy using rule-based intelligent classification models. Fam Pract Palliat Care. 2023;8:154–164.
MLA Kaya, Mehmet Onur et al. “Analyzing of Iron-Deficiency Anemia in Pregnancy Using Rule-Based Intelligent Classification Models”. Family Practice and Palliative Care, vol. 8, no. 6, 2023, pp. 154-6, doi:10.22391/fppc.1347373.
Vancouver Kaya MO, Yıldırım R, Yakar B, Alatas B. Analyzing of iron-deficiency anemia in pregnancy using rule-based intelligent classification models. Fam Pract Palliat Care. 2023;8(6):154-6.
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Family Practice and Palliative Care       ISSN 2458-8865       E-ISSN 2459-1505