Volume 9 • Issue 1 • PP: 15–22 • 2025
Developing a Hybrid Machine Learning Algorithm for Anemia Diagnosis
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© 2025 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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This article was handled by an independent editor. The author serving on the Editorial Board had no involvement in the peer-review process or the final editorial decision.
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The utilization of artificial intelligence (AI) algorithms has significantly transformed the field of blood disease diagnosis, enabling enhanced capabilities in prediction, categorization, and optimization. However, there is still a lack of research exploring hybrid machine learning models that combine qualitative and quantitative datasets to address issues associated with blood diseases. We evaluate algorithmic combinations using datasets that include key characteristics from complete blood count (CBC) examinations. Prominent deep learning models, including CNN, RNN, and RCNN, are assessed. XGBoost is identified as the strongest machine learning algorithm and RCNN as the strongest deep learning model. We therefore propose RCNNX, a hybrid model integrating Robust Scaler, SelectKBest feature selection, RCNN, and XGBoost. RCNNX achieves testing accuracies of 100% and 95.12% on the Anemia Diagnosis Dataset and a second dataset, respectively, with recall rates of 100% and 94.64%. These findings indicate that feature selection reduces the number of input variables and the risk of overfitting, while XGBoost improves the predictive accuracy of RCNN.
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