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Retrospective Examination of Risk Factors Affecting Iron Deficiency Anemia Using Machine Learning Methods

Yıl 2024, Cilt: 45 Sayı: 2, 444 - 448, 30.06.2024

Erol Terzi Bünyamin Sarıbacak Mehmet Şirin Ateş

https://doi.org/10.17776/csj.1326496

Öz

Iron deficiency anemia is one of the most common types of anemia worldwide. In recent years, new developments in the field of medicine have offered early diagnosis and treatment opportunities for anemia patients. In the field of data science, in parallel with the developments in medicine, significant developments are taking place in subjects such as data collection, storage, processing, and reporting. Interdisciplinary joint studies positively contribute to patients’ quality of life and lifespan. In this study, the accuracy of the statistical results was tested with Machine Learning Method (MLM) while investigating the factors that affect the correct prediction of Iron Deficiency Anemia (IDA) diagnosis. In the first stage, the relationships between all variables in the data set and their effects on the differentiation of disease groups were investigated using univariate and multivariate statistical methods. In the second step, the data set was analyzed in detail using four different methods with Artificial Neural Network (ANN) classifier. Weka 3.8 application was preferred for these operations. In the last stage, the results obtained in both stages were compared. Accordingly, it has been observed that hemoglobin (Hb), mean cell volume (MCV), iron (Fe), and ferritin (FERR) have more effects on IDA. ANN (98.06%) is a better discriminator with a correct classification rate

Anahtar Kelimeler

Iron deficiency anemia Logistic regression analysis Artificial neural network Machine learning

Kaynakça

  • [1] World Health Organization. Anemia, (2017), https://www.who.int/health-topics/anaemia#tab=tab_1, Accessed 18 Nov 2022.
  • [2] Allali S., Brousse V., Sacri A. S., Chalumeau M., De Montalembert M., Anemia in Children: Prevalence, Causes, Diagnostic Work-up, and Long-Term Consequences, Expert Review of Hematology, 10(11) (2017) 1023-1028.
  • [3] Cusick S. E., Georgieff M. K., Rao R., Approaches for Reducing the Risk of Early-Life Iron Deficiency-Induced Brain Dysfunction in Children, Nutrients 10 (2) (2018) 227.
  • [4] Andro M., Le Squere P., Estivin S., Gentric A., Anaemia and Cognitive Performances in The Elderly: A Systematic Review, European Journal of Neurology, 20(9) (2013) 1234-1240.
  • [5] Haas J.D., Brownlie T., 4th. Iron Deficiency and Reduced Work Capacity: A Critical Review of the Research to Determine a Causal Relationship, Journal of Nutrition, 131 (2S-2) (2001) 676-690.
  • [6] Hosmer D.W., Lemeshow S., Applied Logistic Regression, John Wiley & Sons, 8-36, New York, 1989.
  • [7] Khan J. R., Chowdhury S., Islam H., Raheem E., Machine Learning Algorithms to Predict the Childhood Anemia in Bangladesh, Journal of Data Science, 17 (1) (2019) 195-218.
  • [8] Schapire R. E., The Boosting Approach to Machine Learning: An Overview, In Nonlinear Estimation and Classification,149-171, Springer, New York, 2003.
  • [9] Kumar, N., Narayan Das, N., Gupta, D., Gupta, K., & Bindra, J. (2021). Efficient automated disease diagnosis using machine learning models, Journal of Healthcare Engineering, 1 (2021) 9983652.
  • [10] Alpar R., Applied Multivariate Statistical Methods (Fourth Edition), Detail Publishing, 637-659, Ankara, 2013.
  • [11] Beam, A. L., Manrai, A. K., & Ghassemi, M. (2020). Challenges to the reproducibility of machine learning models in health care, Jama, 323 (4) 305-306.
  • [12] Mertler C. A., Vannatta R. A., Advanced and Multivariate Statistical Methods: Practical Application and Interpretation, Pyrczak Publishing, Glendale, 2005.
  • [13] Bland J. M., Altman D.G., The Odds Ratio, BMJ, 320 (7247) (2000) 1468.
  • [14] Haykin S. S., Neural Networks and Learning Machines, Simon Haykin, 2009.
  • [15] Hall M. A., Correlation-Based Feature Subset Selection for Machine Learning. Hamilton, New Zealand, 1998.
  • [16] Witten I. H., Frank E., Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Acm Sigmod Record, 31(1) (2002) 76-77.
  • [17] http://saedsayad.com/artificial_neural_network.htm, Accessed 13 June 2024

Yıl 2024, Cilt: 45 Sayı: 2, 444 - 448, 30.06.2024

Erol Terzi Bünyamin Sarıbacak Mehmet Şirin Ateş

https://doi.org/10.17776/csj.1326496

Öz

Kaynakça

  • [1] World Health Organization. Anemia, (2017), https://www.who.int/health-topics/anaemia#tab=tab_1, Accessed 18 Nov 2022.
  • [2] Allali S., Brousse V., Sacri A. S., Chalumeau M., De Montalembert M., Anemia in Children: Prevalence, Causes, Diagnostic Work-up, and Long-Term Consequences, Expert Review of Hematology, 10(11) (2017) 1023-1028.
  • [3] Cusick S. E., Georgieff M. K., Rao R., Approaches for Reducing the Risk of Early-Life Iron Deficiency-Induced Brain Dysfunction in Children, Nutrients 10 (2) (2018) 227.
  • [4] Andro M., Le Squere P., Estivin S., Gentric A., Anaemia and Cognitive Performances in The Elderly: A Systematic Review, European Journal of Neurology, 20(9) (2013) 1234-1240.
  • [5] Haas J.D., Brownlie T., 4th. Iron Deficiency and Reduced Work Capacity: A Critical Review of the Research to Determine a Causal Relationship, Journal of Nutrition, 131 (2S-2) (2001) 676-690.
  • [6] Hosmer D.W., Lemeshow S., Applied Logistic Regression, John Wiley & Sons, 8-36, New York, 1989.
  • [7] Khan J. R., Chowdhury S., Islam H., Raheem E., Machine Learning Algorithms to Predict the Childhood Anemia in Bangladesh, Journal of Data Science, 17 (1) (2019) 195-218.
  • [8] Schapire R. E., The Boosting Approach to Machine Learning: An Overview, In Nonlinear Estimation and Classification,149-171, Springer, New York, 2003.
  • [9] Kumar, N., Narayan Das, N., Gupta, D., Gupta, K., & Bindra, J. (2021). Efficient automated disease diagnosis using machine learning models, Journal of Healthcare Engineering, 1 (2021) 9983652.
  • [10] Alpar R., Applied Multivariate Statistical Methods (Fourth Edition), Detail Publishing, 637-659, Ankara, 2013.
  • [11] Beam, A. L., Manrai, A. K., & Ghassemi, M. (2020). Challenges to the reproducibility of machine learning models in health care, Jama, 323 (4) 305-306.
  • [12] Mertler C. A., Vannatta R. A., Advanced and Multivariate Statistical Methods: Practical Application and Interpretation, Pyrczak Publishing, Glendale, 2005.
  • [13] Bland J. M., Altman D.G., The Odds Ratio, BMJ, 320 (7247) (2000) 1468.
  • [14] Haykin S. S., Neural Networks and Learning Machines, Simon Haykin, 2009.
  • [15] Hall M. A., Correlation-Based Feature Subset Selection for Machine Learning. Hamilton, New Zealand, 1998.
  • [16] Witten I. H., Frank E., Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Acm Sigmod Record, 31(1) (2002) 76-77.
  • [17] http://saedsayad.com/artificial_neural_network.htm, Accessed 13 June 2024
Toplam 17 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Risk Analizi
Bölüm Natural Sciences
Yazarlar

Erol Terzi 0000-0002-2309-827X

Bünyamin Sarıbacak 0000-0003-2775-776X

Mehmet Şirin Ateş 0000-0001-9904-6380

Yayımlanma Tarihi 30 Haziran 2024
Gönderilme Tarihi 13 Temmuz 2023
Kabul Tarihi 22 Haziran 2024
Yayımlandığı Sayı Yıl 2024Cilt: 45 Sayı: 2

Kaynak Göster

APA Terzi, E., Sarıbacak, B., & Ateş, M. Ş. (2024). Retrospective Examination of Risk Factors Affecting Iron Deficiency Anemia Using Machine Learning Methods. Cumhuriyet Science Journal, 45(2), 444-448. https://doi.org/10.17776/csj.1326496
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