• Gregorius Airlangga Information System Study Program, Engineering Faculty, Universitas Katolik Indonesia Atma Jaya, Indonesia
Keywords: Anemia Classification, Complete Blood Count(CBC), Convolutional Neural Network (CNN), Deep Learning, Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN)


Anemia is a common condition marked by a deficiency in red blood cells or hemoglobin, affecting the body's ability to deliver oxygen to tissues. Accurate and timely diagnosis is essential for effective treatment. This study aims to classify different types of anemia using complete blood count (CBC) data through the application of deep learning models. We evaluated the performance of four deep learning architectures: Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Fully Connected Network (FCN). The dataset included CBC parameters such as hemoglobin, platelet count, and white blood cell count, labeled with anemia types. Our results indicate that CNN and FCN models achieved the highest test accuracies of 0.85, outperforming MLP and RNN models. This superior performance is due to the ability of CNN and FCN to capture complex patterns and spatial relationships within CBC data. Techniques like data augmentation and weighted loss functions were employed to address class imbalance. These findings demonstrate the potential of deep learning models to automate anemia diagnosis, thereby enhancing clinical decision-making and patient outcomes.


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D. Kinyoki, A. E. Osgood-Zimmerman, N. V Bhattacharjee, N. J. Kassebaum, and S. I. Hay, “Anemia prevalence in women of reproductive age in low-and middle-income countries between 2000 and 2018,” Nat. Med., vol. 27, no. 10, pp. 1761–1782, 2021.

S. A. Ali, U. S. Khan, and A. S. Feroz, “Prevalence and determinants of anemia among women of reproductive age in developing countries,” 2020.

S. Bathla and S. Arora, “Prevalence and approaches to manage iron deficiency anemia (IDA),” Crit. Rev. Food Sci. Nutr., vol. 62, no. 32, pp. 8815–8828, 2022.

M. D. Cappellini, K. M. Musallam, and A. T. Taher, “Iron deficiency anaemia revisited,” J. Intern. Med., vol. 287, no. 2, pp. 153–170, 2020.

R. Lassila and J. W. Weisel, “Role of red blood cells in clinically relevant bleeding tendencies and complications,” J. Thromb. Haemost., 2023.

T. Sonnweber, A. Pizzini, I. Tancevski, J. Löffler-Ragg, and G. Weiss, “Anaemia, iron homeostasis and pulmonary hypertension: a review,” Intern. Emerg. Med., vol. 15, pp. 573–585, 2020.

D. T. Lee and M. L. Plesa, “Anemia,” in Family Medicine: Principles and Practice, Springer, 2022, pp. 1815–1829.

R. S. Hussien, S. I. A. Jabuk, Z. M. Altaee, and A. M. K. Al-Maamori, “REVIEW OF ANEMIA: TYPES AND CAUSES,” 2023.

M. Badireddy, K. M. Baradhi, and A. Wilhite Hughes, “Chronic Anemia (Nursing),” 2021.

A. Al-Naseem, A. Sallam, S. Choudhury, and J. Thachil, “Iron deficiency without anaemia: a diagnosis that matters,” Clin. Med. (Northfield. Il)., vol. 21, no. 2, p. 107, 2021.

B. J. Bain, Blood cells: a practical guide. John Wiley & Sons, 2021.

L. Kamal and R. J. R. Raj, “Harnessing deep learning for blood quality assurance through complete blood cell count detection,” e-Prime-Advances Electr. Eng. Electron. Energy, p. 100450, 2024.

H. K. Bharadwaj et al., “A review on the role of machine learning in enabling IoT based healthcare applications,” IEEE Access, vol. 9, pp. 38859–38890, 2021.

V. Singh, S.-S. Chen, M. Singhania, B. Nanavati, A. Gupta, and others, “How are reinforcement learning and deep learning algorithms used for big data based decision making in financial industries--A review and research agenda,” Int. J. Inf. Manag. Data Insights, vol. 2, no. 2, p. 100094, 2022.

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, 2024.

A. Joshi, P. Saggar, R. Jain, M. Sharma, D. Gupta, and A. Khanna, “CatBoost—An ensemble machine learning model for prediction and classification of student academic performance,” Adv. Data Sci. Adapt. Anal., vol. 13, no. 03n04, p. 2141002, 2021.

W. Jia, M. Sun, J. Lian, and S. Hou, “Feature dimensionality reduction: a review,” Complex & Intell. Syst., vol. 8, no. 3, pp. 2663–2693, 2022.

M. M. Nair, S. Kumari, A. K. Tyagi, and K. Sravanthi, “Deep learning for medical image recognition: open issues and a way to forward,” in Proceedings of the Second International Conference on Information Management and Machine Intelligence: ICIMMI 2020, 2021, pp. 349–365.

F. Piccialli, V. Di Somma, F. Giampaolo, S. Cuomo, and G. Fortino, “A survey on deep learning in medicine: Why, how and when?,” Inf. Fusion, vol. 66, pp. 111–137, 2021.

M. Li, Y. Jiang, Y. Zhang, and H. Zhu, “Medical image analysis using deep learning algorithms,” Front. Public Heal., vol. 11, p. 1273253, 2023.

L. Gaur, M. Bhandari, T. Razdan, S. Mallik, and Z. Zhao, “Explanation-driven deep learning model for prediction of brain tumour status using MRI image data,” Front. Genet., vol. 13, p. 822666, 2022.

J. Eom et al., “Deep-learned spike representations and sorting via an ensemble of auto-encoders,” Neural Networks, vol. 134, pp. 131–142, 2021.

R. Gaikar, “Development of Machine Learning Algorithms for Kidney Cancer Diagnosis from Multi-Parametric MRI and Histopathology Images,” University of Guelph, 2023.

R. Levin, Applications of Optimization and Machine Learning to Healthcare. University of Washington, 2022.

S. Cleveland, “The Future of Diagnosis: Achieving Excellence and Equity,” Planning, 2023.

P. Dutta, P. Upadhyay, M. De, and R. G. Khalkar, “Medical image analysis using deep convolutional neural networks: CNN architectures and transfer learning,” in 2020 International Conference on Inventive Computation Technologies (ICICT), 2020, pp. 175–180.

W. Li, M. Zuo, H. Zhao, Q. Xu, and D. Chen, “Prediction of coronary heart disease based on combined reinforcement multitask progressive time-series networks,” Methods, vol. 198, pp. 96–106, 2022.

H. Koch et al., “CLIMB: High-dimensional association detection in large scale genomic data,” Nat. Commun., vol. 13, no. 1, p. 6874, 2022.

M. S. Pingel, “Leveraging machine learning and process mining to predict anaemia with the help of biomarker data,” University of Twente, 2021.

O. Saidani et al., “White blood cells classification using multi-fold pre-processing and optimized CNN model,” Sci. Rep., vol. 14, no. 1, p. 3570, 2024.

M. Kirschenbaum, “Spec Acts: Reading Form in Recurrent Neural Networks,” ELH, vol. 88, no. 2, pp. 361–386, 2021.

F. H. Awad, M. M. Hamad, and L. Alzubaidi, “Robust classification and detection of big medical data using advanced parallel k-means clustering, yolov4, and logistic regression,” Life, vol. 13, no. 3, p. 691, 2023.

J. W. Asare, P. Appiahene, E. T. Donkoh, and G. Dimauro, “Iron deficiency anemia detection using machine learning models: A comparative study of fingernails, palm and conjunctiva of the eye images,” Eng. Reports, vol. 5, no. 11, p. e12667, 2023.

C. Janiesch, P. Zschech, and K. Heinrich, “Machine learning and deep learning,” Electron. Mark., vol. 31, no. 3, pp. 685–695, 2021.

J. Krois et al., “Generalizability of deep learning models for dental image analysis,” Sci. Rep., vol. 11, no. 1, p. 6102, 2021.

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