Çocukluk Çağı Anemisinin Tahmininde Aşırı Örnekleme Yöntemlerini Kullanan Makine Öğrenmesi Algoritmalarının Performans Karşılaştırması

Yıl 2025, Cilt: 15 Sayı: 3, 1 - 11, 19.11.2025

Kadriye Filiz Balbal

Öz

Çocukluk çağı anemisi önemli bir sağlık sorunudur. Okul öncesi çağdaki çocuklarda sık görülen anemi, bu yaş grubunda fiziksel ve zihinsel gelişimsel gecikmelere neden olur. Yapay zeka gibi son teknoloji yöntemlerle önlenebilir ve tedavi edilebilir bu sağlık sorunlarının ele alınması ve araştırılması önemlidir. Bu nedenle, bu çalışmada Nijerya'da 0-59 aylık çocuklarda anemi düzeylerini tahmin etmek için yapay zekanın bir alt alanı olan makine öğrenmesi teknikleri kullanılmıştır. Çocukluk çağı anemi düzeylerini tahmin etmede sorunlara neden olabilen veri dengesizliği sorununu ele almak için çalışmada SMOTE ve ADASYN aşırı örnekleme teknikleri kullanılmıştır. Yeni elde edilen dengeli verilerle yapılan analizlerde, SMOTE ve ADASYN yöntemlerinin tüm ML modelleri için dengesiz verilerle elde edilen sonuçlardan önemli ölçüde daha iyi performans gösterdiği görülmüştür. Çalışmada kullanılan tüm ML algoritmalarının doğruluk, kesinlik, geri çağırma ve F1 puanı metrikleri açısından ortalama sonuçları, aşırı örnekleme yöntemleriyle karşılaştırıldığında, tüm metrikler açısından en başarılı sonuç SMOTE yöntemi ile elde edilmiştir.

Anahtar Kelimeler

Yapay zeka , makine öğrenmesi , ADASYN , SMOTE , çocukluk çağı anemisi

Performance Comparison of Machine Learning Algorithms Using Oversampling Methods to Predict Childhood Anemia

Öz

Childhood anemia is a major health problem. Anemia, which is common in preschool-aged children, causes physical and mental developmental delays in this age group. It is important to address and investigate these preventable and treatable health problems using state-of-the-art methods such as artificial intelligence. Therefore, this study employs machine learning techniques, a subfield of artificial intelligence, to predict the anemia levels in children aged 0–59 months in Nigeria. To address the issue of data imbalance, which can cause problems in estimating childhood anemia levels, the SMOTE and ADASYN oversampling techniques were employed in this study. In the analyses performed with the newly obtained balanced data, it was observed that the SMOTE and ADASYN methods performed significantly better than the results obtained with imbalanced data for all ML models. When the average results of all ML algorithms used in this study in terms of accuracy, precision, recall, and F1 score metrics are compared to the oversampling methods, the most successful result in terms of all metrics was obtained with the SMOTE method.

Anahtar Kelimeler

Artificial intelligence , machine learning , ADASYN , SMOTE , childhood anemia

Kaynakça

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