Pre-collection Prediction of Human Leukocyte Antigen Rarity in Cord Blood Banking: A Comparative Evaluation of Machine Learning Models

Articles in Press

Document Type : Research Article

Authors

1 Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, No. 43, Shahid Mofatteh Ave, Tehran, Iran

2 Department of Stem Cells and Developmental Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran

Abstract

Human leukocyte antigen (HLA) compatibility is critical for the success of hematopoietic stem cell transplantation, particularly for patients without matched related donors. As cord blood units (CBUs) play a growing role in bridging donor gaps, cord blood banks must optimize donor selection and CBUs’ storage to maximize HLA diversity. A prenatal, pre-collection prediction framework is developed to estimate whether a CBU’s future HLA profile will be rare or common within a large banking registry. Rarity was defined as a binary label based on observed 6/6 low-resolution matches at the HLA-A, HLA-B, and HLA-DRB1 loci (0 matches = rare; ≥1 = common). Demographic, medical, and obstetric features were preprocessed via one-hot encoding, degree-2 polynomial expansion, and Principal Component Analysis (PCA); feature selection used Random Forest (RF) importance within a leakage-controlled pipeline. Five machine learning models, including K-Nearest Neighbors (KNN), RF, XGBoost, and a deep multilayer perceptron (MLP), are used, with each evaluated with and without Genetic Algorithm (GA) hyperparameter tuning on identical cross-validation folds, and a TOPSIS multi-criteria ranking was performed to rank algorithms. The GA-tuned MLP performs best (Accuracy 0.74, ROC-AUC 0.84) and ranks first by Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), with GA-tuned XGBoost close behind. These results demonstrate that prenatal, pre-collection prediction of HLA rarity is feasible and can support prioritization for confirmatory genotyping to enhance diversity in cord-blood inventories.

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Scientia Iranica

Articles in Press, Accepted Manuscript
Available Online from 10 June 2026

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History

  • Receive Date: 13 July 2025
  • Revise Date: 14 November 2025
  • Accept Date: 09 February 2026

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