MDS-AN: An attention-based explainable model for detecting myelodysplastic syndrome using hematological variables

This study aims to develop and evaluate a deep learning model, which is based on an attention architecture for automatic detection of Myelodysplastic Syndromes (MDS), using age, sex and numerical values of 19 variables obtained from peripheral blood samples on a hematologic analyzer used clinical laboratory workflow. A dataset was collected from a total of 223 patients, 121 with MDS and 102 without this condition. The dataset was divided into training, validation, and test sets to train and validate the system. The classification model was developed from scratch using a attention-based architecture specifically designed to handle 21 input variables. In addition, a separate final evaluation was performed with a new group of 150 patients (56 with MDS and 94 with non-MDS) to determine a range of uncertainty and performance metrics. On the test set, the model reached an accuracy of 97.5%, with 95% sensitivity, 95% specificity, 100% precision, and an F1-score of 0.97 for patients with MDS. In the independent evaluation, the model obtained 94.34% sensitivity, 95.60% specificity, 92.59% precision, and an F1-score of 0.935 after applying the uncertainty interval. The explainability analysis, based on Integrated Gradients, SHAP, pairwise interaction analysis, and internal attention mechanisms, consistently highlighted hematological variables associated with MDS, supporting the clinical interpretability of the model’s decisions. The proposed approach enables the automated differentiation of patients with MDS from those with non-MDS. In addition, the integration of explainability techniques strengthens the potential use of the system as a support tool in the clinical recognition of MDS. • A deep learning model for the detecting of MDS using quantitative hematological variables. • Evaluation with independent patients, determining a decision rule and an uncertainty range. • Explainability analysis to identify the most influential variables in the classification. • Concordance between explainability results and established clinical diagnostic criteria for MDS.