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Journals / Black Sea Journal of Engineering and Science / 2026 / Cilt: 9 Sayı: 4

Multiclassification of Hb E Carrier Status and Thalassaemia Patterns Based on Routine Complete Blood Counts Using Ensemble Learning

Hb E Taşıyıcılığı ve Talasemi Örüntülerinin Rutin Tam Kan Sayımına Dayalı Topluluk Öğrenmesi İle Çok Sınıflı Değerlendirilmesi

Mert Basancelebi(Bülent Ecevit Üniversitesi)0009-0003-4611-3326
Rukiye Uzun Arslan(BÜLENT ECEVİT ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ)0000-0002-2082-8695
İrem Şenyer Yapıcı(ZONGULDAK BÜLENT ECEVİT ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ)0000-0003-0655-340X
Pages
1665–1681
Publication Date
2026-07-15
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The highest overall classification performance on the test dataset was achieved with the Bagging model; the model produced 0.799 accuracy, 0.920 specificity, and 0.660 Matthews correlation coefficient.

📌 3 Key Takeaways

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Objective & Hypothesis:In this study, we investigated the classifiability of individuals into four clinical categories—normal hematological profile, possible α-thalassemia pattern, Hb E carrier status, and other thalassemic/hemoglobinopathic patterns—using routine complete blood count (CBC) parameters.
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Key Findings & Data:In this context, the performance of different machine learning (ML) methods in distinguishing clinical patterns that may overlap in terms of hematological parameters was evaluated comparatively.
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Conclusion & Impact:The highest overall classification performance on the test dataset was achieved with the Bagging model; the model produced 0.799 accuracy, 0.920 specificity, and 0.660 Matthews correlation coefficient.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Hb E taşıyıcılığı · Talasemi · Tam kan sayımı

Abstract

In this study, we investigated the classifiability of individuals into four clinical categories—normal hematological profile, possible α-thalassemia pattern, Hb E carrier status, and other thalassemic/hemoglobinopathic patterns—using routine complete blood count (CBC) parameters. In this context, the performance of different machine learning (ML) methods in distinguishing clinical patterns that may overlap in terms of hematological parameters was evaluated comparatively. For this purpose, an open-access dataset comprising 5,778 individuals was used; 20 numerical hematological variables and the gender variable were included in the modeling process. The study comparatively analyzed Bagging, Random Forest, CatBoost, Extra Trees, XGBoost, and Multi-Layer Perceptron models. The dataset was split into 80% training and 20% independent test data, and 5-fold cross-validation and hyperparameter optimization were applied during the model development process. Model performances were evaluated using accuracy, specificity, false positive rate, Matthews correlation coefficient, Cohen’s Kappa coefficient, and ROC-AUC metrics. The highest overall classification performance on the test dataset was achieved with the Bagging model; the model produced 0.799 accuracy, 0.920 specificity, and 0.660 Matthews correlation coefficient. In the ROC-AUC analysis, the CatBoost model reached the highest threshold-independent discriminative level with a micro-average AUC value of 0.956. Furthermore, SHapley Additive exPlanations (SHAP) analyses demonstrated that hematological parameters such as MCV, MCH, RDW, and MCHC made significant contributions to the classification process. The findings reveal that tree-based ensemble learning models offer more balanced and stable performance in distinguishing four-class hematological patterns. In conclusion, ML approaches based on routine CBC parameters can serve as a low-cost, accessible, and data-driven decision-support tool for the preliminary assessment of hemoglobinopathy and thalassemia patterns.

Özet

Bu çalışmada, rutin tam kan sayımı (TKS) parametreleri kullanılarak bireylerin normal hematolojik profil, olası α-talasemi paterni, Hb E taşıyıcılığı ve diğer talasemik/hemoglobinopatik örüntüler olmak üzere dört klinik sınıf altında sınıflandırılabilirliği araştırılmıştır. Bu kapsamda, hematolojik parametreler bakımından birbiriyle örtüşebilen klinik örüntülerin ayırt edilmesinde farklı makine öğrenmesi (MÖ) yöntemlerinin performansı karşılaştırmalı olarak değerlendirilmiştir. Bu amaçla 5778 bireye ait açık erişimli veri seti kullanılmış; 20 sayısal hematolojik değişken ve cinsiyet değişkeni modelleme sürecine dâhil edilmiştir. Çalışmada Torbalama, Rastgele Orman, CatBoost, Ekstra Ağaçlar, XGBoost ve Çok Katmanlı Algılayıcı modelleri karşılaştırmalı olarak incelenmiştir. Veri seti %80 eğitim ve %20 bağımsız test verisi olarak ayrılmış, model geliştirme sürecinde 5 katlı çapraz doğrulama ve hiperparametre optimizasyonu uygulanmıştır. Model performansları doğruluk, özgüllük, yanlış pozitif oranı, Matthews korelasyon katsayısı (MCC), Cohen’s Kappa katsayısı ve ROC-AUC ölçütleriyle değerlendirilmiştir. Test veri seti üzerinde en yüksek genel sınıflandırma performansı Torbalama modeli ile elde edilmiş; model 0,799 doğruluk, 0,920 özgüllük ve 0,660 MCC üretmiştir. ROC-AUC analizinde ise CatBoost modeli 0,956 mikro ortalama AUC değeriyle en yüksek eşik-bağımsız ayırt edicilik düzeyine ulaşmıştır. Ayrıca SHapley Additive exPlanations (SHAP) analizleri, sınıflandırma sürecinde MCV, MCH, RDW ve MCHC gibi hematolojik parametrelerin belirgin katkı sağladığını göstermiştir. Bulgular, ağaç tabanlı topluluk öğrenmesi modellerinin dört sınıflı hematolojik örüntülerin ayırt edilmesinde daha dengeli ve kararlı performans sunduğunu ortaya koymaktadır. Sonuç olarak, rutin TKS parametrelerine dayalı MÖ yaklaşımları, hemoglobinopati ve talasemi örüntülerinin ön değerlendirilmesinde düşük maliyetli, erişilebilir ve veri temelli bir karar destek aracı olarak kullanılabilir.

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Cite This Article

Basancelebi, M., Arslan, R. U., Yapıcı, İ. Ş. (2026). Multiclassification of Hb E Carrier Status and Thalassaemia Patterns Based on Routine Complete Blood Counts Using Ensemble Learning. Black Sea Journal of Engineering and Science, Cilt: 9 Sayı: 4, 1665–1681. https://doi.org/10.34248/bsengineering.1945642

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