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
Iron deficiency anemia Logistic regression analysis Artificial neural network Machine learning
References
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Abstract
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.
- [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
Details
| Primary Language | English |
|---|---|
| Subjects | Risk Analysis |
| Journal Section | Natural Sciences |
| Authors | |
| Publication Date | June 30, 2024 |
| Submission Date | July 13, 2023 |
| Acceptance Date | June 22, 2024 |
| Published in Issue | Year 2024 |