Year: 2025
Type: article
Abstract: Over 1.6 billion people worldwide suffer from anemia, a common hematologic disorder that goes undiagnosed in situations with limited resources due to a lack of diagnostic tools. This study presents a machine learning approach to the lowcost, non-invasive diagnostic method of detecting anemia from complete blood count (CBC) data. Using high-quality CBC data, we compare the predictability of three supervised models: Random Forest, Support Vector Machine (SVM), and Logistic Regression. GridSearchCV was used for extensive preprocessing, hyperparameter tuning, and stratified cross-validation techniques. Among the models compared, Random Forest had the highest accuracy of 99.48%, outperforming the SVM model (23.81%) and the Logistic Regression model (57.23%). SHAP (SHapley Additive exPlanations), which has a strong correlation with clinical relevance, was used to select the most contributing features influencing predictions in order to enhance model interpretability. Our results show that interpretability and ensemble learning can work well together as a diagnostic support system for the early identification of anemia in clinical settings.
Author: Prerna Samtani
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4412518334