Classification of Anemia Severity Using Random Forest and SHAP Analysis on Complete Blood Count Data
DOI:
https://doi.org/10.35870/ijsecs.v6i1.7199Keywords:
Anemia Severity Classification, Random Forest, SHAP, Explainable Artificial Intelligence, Complete Blood CountAbstract
Anemia remains a major global public health challenge, requiring accurate severity classification to support early diagnosis and clinical decision-making, particularly in resource-limited settings. This study aimed to develop an accurate and interpretable multiclass model for classifying anemia severity according to the 2011 WHO hemoglobin thresholds. A Random Forest model combined with SHAP (SHapley Additive exPlanations) analysis was developed and evaluated using a Complete Blood Count (CBC) dataset comprising 364 samples categorized into four classes: Normal, Mild Anemia, Moderate Anemia, and Severe Anemia. Data preprocessing included Interquartile Range (IQR) capping, label encoding, and StandardScaler standardization. The model was optimized through Grid Search Cross-Validation using 432 hyperparameter combinations and five-fold Stratified K-Fold validation. On the test set, the model achieved an accuracy of 95.89%, macro-precision of 96.61%, macro-recall of 97.14%, and macro-F1-score of 96.85%. The Moderate and Severe Anemia classes achieved F1-scores of 100%, although the result for Severe Anemia should be interpreted cautiously because of the limited number of test samples. Mild Anemia was the most challenging class, particularly for samples near the hemoglobin classification thresholds. SHAP analysis identified HGB, PCV, and RBC as the most influential features. However, the SHAP results should be interpreted in light of the correlations among these variables and the use of HGB as the basis for WHO severity labeling. The findings indicate that Random Forest combined with SHAP can accurately reproduce anemia severity classifications based on the applied labeling criteria while providing interpretable predictions. Further external and clinical validation is required before the model can be considered for use in a clinical decision support system.
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