EN
Retrospective Examination of Risk Factors Affecting Iron Deficiency Anemia Using Machine Learning Methods
Abstract
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
Keywords
References
- [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.
Details
Primary Language
English
Subjects
Risk Analysis
Journal Section
Research Article
Publication Date
June 30, 2024
Submission Date
July 13, 2023
Acceptance Date
June 22, 2024
Published in Issue
Year 2024 Volume: 45 Number: 2