A Comparative Analysis of Machine Learning Classifier of Anemia Diagnosis Based on Complete Blood Count (CBC) Data
DOI:
https://doi.org/10.47738/ijiis.v8i4.286Keywords:
Anemia classification, Complete Blood Count (CBC), Machine Learning, XGBoost, SVM, Naive Bayes, Medical DiagnosisAbstract
Anemia is a prevalent hematological condition that requires accurate and timely diagnosis to ensure effective treatment. This study aims to compare the performance of several machine learning algorithms Random Forest, Support Vector Machine (SVM), Naive Bayes, and XGBoost in classifying different types of anemia based on Complete Blood Count (CBC) data. The dataset includes three diagnostic categories: Healthy, Normocytic hypochromic anemia, and Normocytic normochromic anemia. After preprocessing and normalization, each model was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC. The results show that XGBoost achieved the highest overall performance with 99% accuracy and a perfect AUC of 1.00, followed closely by SVM and Naive Bayes. Naive Bayes showed lower performance, particularly in identifying normocytic normochromic anemia. These findings suggest that machine learning, especially ensemble-based models, holds strong potential in supporting clinical diagnosis of anemia using CBC data.References
T. Karagül Yıldız, N. Yurtay, and B. Öneç, “Classifying anemia types using artificial learning methods,” Engineering Science and Technology, an International Journal, vol. 24, no. 1, pp. 50–70, Feb. 2021, doi: 10.1016/j.jestch.2020.12.003.
M. N. Garcia-Casal, O. Dary, M. E. Jefferds, and S. R. Pasricha, “Diagnosing anemia: Challenges selecting methods, addressing underlying causes, and implementing actions at the public health level,” Jun. 01, 2023, John Wiley and Sons Inc. doi: 10.1111/nyas.14996.
J. G. Gómez, C. Parra Urueta, D. S. Álvarez, V. Hernández Riaño, and G. Ramirez-Gonzalez, “Anemia Classification System Using Machine Learning,” Informatics, vol. 12, no. 1, Mar. 2025, doi: 10.3390/informatics12010019.
N. J. Kassebaum et al., “A systematic analysis of global anemia burden from 1990 to 2010,” Blood 123, vol. 123, no. 5, pp. 615–624, Jul. 2025, doi: 10.1182/blood-2013-06.
L. Del Castillo et al., “Prevalence and risk factors of anemia in the mother–child population from a region of the Colombian Caribbean,” BMC Public Health, vol. 23, no. 1, Dec. 2023, doi: 10.1186/s12889-023-16475-0.
J. M. Pineda, “Predictive models in health based on machine learning,” Revista Medica Clinica Las Condes, vol. 33, no. 6, pp. 583–590, Nov. 2022, doi: 10.1016/j.rmclc.2022.11.002.
WHO, “Anaemia in women and children ,” https://www.who.int/data/gho/data/themes/topics/anaemia_in_women_and_children.
M. Domenica Cappellini and I. Motta, “Anemia in Clinical Practice-Definition and Classification: Does Hemoglobin Change With Aging?,” Oct. 01, 2015, W.B. Saunders. doi: 10.1053/j.seminhematol.2015.07.006.
S. E. Calle-Pesántez and J. M. Pallo-Chiguano, “Capítulo 3. Inteligencia Artificial en la comunicación científica,” Espejo de Monografías de Comunicación Social, no. 23, pp. 59–81, May 2024, doi: 10.52495/c3.emcs.23.ti12.
S. Pullakhandam and S. McRoy, “Classification and Explanation of Iron Deficiency Anemia from Complete Blood Count Data Using Machine Learning,” BioMedInformatics, vol. 4, no. 1, pp. 661–672, Mar. 2024, doi: 10.3390/biomedinformatics4010036.
G. Airlangga, “Leveraging Machine Learning for Accurate Anemia Diagnosis Using Complete Blood Count Data,” Indonesian Journal of Artificial Intelligence and Data Mining, vol. 7, no. 2, p. 318, May 2024, doi: 10.24014/ijaidm.v7i2.29869.
S. S. Abdul-Jabbar, A. K. Farhan, and A. S. Luchinin, “A Comparative Study of Anemia Classification Algorithms for International and Newly CBC Datasets,” International journal of online and biomedical engineering, vol. 19, no. 6, pp. 141–157, 2023, doi: 10.3991/ijoe.v19i06.38157.
A. Végh, L. Takáč, O. Czakóová, K. Dansca, and D. Nagy, “Evaluating Optimizable Machine Learning Models for Anemia Type Prediction from Complete Blood Count Data,” International Journal of Advanced Natural Sciences and Engineering Researches, vol. 7, no. 7, pp. 108–119, 2024, [Online]. Available: https://as-proceeding.com/index.php/ijanser
L. Kabootarizadeh, A. Jamshidnezhad, Z. Koohmareh, and A. Ghamchili, “Differential diagnosis of iron-deficiency anemia from β-thalassemia trait using an intelligent model in comparison with discriminant indexes,” Acta Informatica Medica, vol. 27, no. 2, pp. 78–84, 2019, doi: 10.5455/aim.2019.27.78-84.
H. Benhar, A. Idri, and J. L Fernández-Alemán, “Data preprocessing for heart disease classification: A systematic literature review.,” Oct. 01, 2020, Elsevier Ireland Ltd. doi: 10.1016/j.cmpb.2020.105635.
Y. Kumar, A. Koul, R. Singla, and M. F. Ijaz, “Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda,” J Ambient Intell Humaniz Comput, vol. 14, no. 7, pp. 8459–8486, Jul. 2023, doi: 10.1007/s12652-021-03612-z.
Y. C. Wang, H. M. Song, J. S. Wang, X. R. Ma, Y. W. Song, and Y. L. Qi, “Multi-strategy fusion binary SHO guided by Pearson correlation coefficient for feature selection with cancer gene expression data,” Egyptian Informatics Journal, vol. 29, Mar. 2025, doi: 10.1016/j.eij.2025.100639.
K. M. M. Uddin, A. Al Mamun, A. Chakrabarti, and R. Mostafiz, “An ensemble machine learning-based approach to predict thyroid disease using hybrid feature selection,” Biomedical Analysis, vol. 1, no. 3, pp. 229–239, Sep. 2024, doi: 10.1016/j.bioana.2024.08.001.
F. Palace-Berl et al., “5-Nitro-2-furfuriliden derivatives as potential anti-Trypanosoma cruzi agents: Design, synthesis, bioactivity evaluation, cytotoxicity and exploratory data analysis,” Bioorg Med Chem, vol. 21, no. 17, pp. 5395–5406, Sep. 2013, doi: 10.1016/j.bmc.2013.06.017.
E. Dritsas and M. Trigka, “Data-Driven Machine-Learning Methods for Diabetes Risk Prediction,” Sensors, vol. 22, no. 14, Jul. 2022, doi: 10.3390/s22145304.
Q. An, S. Rahman, J. Zhou, and J. J. Kang, “Machine Learning in Healthcare Industry,” 2021. [Online]. Available: https://encyclopedia.pub/entry/43803
A. Sirag and N. M. Nor, “Out-of-pocket health expenditure and poverty: Evidence from a dynamic panel threshold analysis,” Healthcare (Switzerland), vol. 9, no. 5, May 2021, doi: 10.3390/healthcare9050536.
Ş. K. Çorbacıoğlu and G. Aksel, “Receiver operating characteristic curve analysis in diagnostic accuracy studies: A guide to interpreting the area under the curve value,” Oct. 01, 2023, Wolters Kluwer Medknow Publications. doi: 10.4103/tjem.tjem_182_23.
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