Predicting Anemia Using Tree-Based Classification Algorithms and Feature Selection

Salman, Ihsan; Ahmad, Arshed; Mohammed, Sahar; Kadhim, Mohammed

doi:10.25673/123647

Proceedings of International Conference on Applied Innovation in IT · 2026/03/31 · Vol. 14 · Issue 1 · pp. 859–868

Predicting Anemia Using Tree-Based Classification Algorithms and Feature Selection

Ihsan Salman, Arshed A. Ahmad, Sahar Jasim Mohammed, Mohammed S. Mohammed and Qusay Kanaan Kadhim

📄 Download PDF DOI: 10.25673/123647

Abstract

The Paper discussed the performance of 3 classification techniques (Hoeffding Tree, Logistic Model Tree, and Random Tree) by applying it on a dataset of 539 samples. This was done using 11 features related to blood tests regarding anemia classes by applying 10-fold cross-validation to each technique with time consideration as well. The process was done firstly without feature minimization and showed that LMT achieved the highest accuracy with about 85.53%, then followed by the Random Tree and the Hoeffding Tree with approximately 1 and 2 differences respectively. Classification time also evaluated for this process with leading amplitude for the Random Tree about 0.02 seconds, then followed by the Hoeffding Tree and the LMT with higher running time due to the long mathematical calculations of LMT and Hoeffding Tree. In the second step, feature selection was applied and the elimination mechanism was done for features that considered as less impact on the prediction results such as Gender, Age, and WB. These combinations of feature selection provided better results for three utilized techniques that reach 96.27% for LMT, 94.42 for the Hoeffding Tree's, and 91.26% for th Random Tree. The results highlighted the impact features on Anemia dataset that should be assembled for such a clinical dataset to provide accuracy improvement and lower processing time.

Keywords

Anemia Logistic Model Tree Hoeffding Tree Random Tree Prediction.

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