A complete blood count-based machine learning model for rapid differentiation of aplastic anemia, immune thrombocytopenia, and myelodysplastic syndromes in routine clinical practice.

Background: Accurate differentiation of common hematologic disorders remains challenging in routine clinical practice and often requires invasive diagnostic procedures. Although complete blood count (CBC) testing is widely available, its diagnostic value for early disease triage has not been fully understood. Methods: Retrospectively, among 165,181 routine blood test records collected between October 2011 and June 2025, 4056 samples with confirmed diagnoses were included for model development and validation after exclusion of cases lacking definitive diagnostic information. Patients were classified into aplastic anemia (AA), immune thrombocytopenia (ITP), myelodysplastic syndrome (MDS), and other hematologic conditions. Machine learning models were developed using routinely available CBC parameters. Model performance was assessed using one-vs-rest receiver operating characteristic (ROC) curves, area under the curve (AUC), and class-specific precision, recall, and F1-scores. Model interpretability was evaluated using Shapley Additive exPlanations (SHAP). Results: Baseline demographic and hematologic parameters differed significantly among diagnostic groups (all Conclusions: A machine learning model based on routinely available CBC parameters can support non-invasive differentiation of common hematologic disorders. This approach may serve as a practical screening and triage tool at the outpatient or pre-bone marrow stage, helping optimize the use of invasive diagnostic procedures.