Machine learning models in predictive factors for megaloblastic character of macrocytic anemia

Background Differentiating megaloblastic from non-megaloblastic macrocytic anemia usually relies on vitamin B12 and/or B9 assessment and bone marrow examination. In resource-limited regions, these diagnostic tools are not readily available. This study aims to develop a machine learning model to distinguish between these two forms of macrocytic anemia using commonly accessible clinical and laboratory parameters. Methods A logistic regression model was developed using a dataset containing clinical features and routine laboratory data. Key predictive variables were identified through the SequentialFeatureSelector. The model was optimized using GridSearchCV and validated through stratified 5-fold cross-validation to ensure robust performance. Results ESR, glossitis, and MCHC were the most influential variables in predicting non-megaloblastic anemia, with respective importance percentages of 23.11 %, 22.07 %, and 13.87 %. Other variables such as LDH (13.77 %), aregenerative status (13.42 %), and limb paresthesia (11.78 %) also contributed significantly to the model, while MCV had a comparatively lower importance (1.98 %). The model demonstrated a high discriminatory ability, with an area under the ROC curve (AUC) of 0.92 and an overall accuracy of 94 %. Learning curve analysis confirmed its stability and consistent performance across varying data sizes. Conclusions By reducing dependence on specialized diagnostic tests and invasive procedures, this approach is particularly suited for resource-constrained healthcare settings.