An Artificial Intelligence-Based Diagnostic System for Acute Lymphoblastic Leukemia Detection.

Journal: Studies in health technology and informatics
PMID: 37387013

Abstract

This study suggests a novel Acute Lymphoblastic Leukemia (ALL) diagnostic model, built solely on complete blood count (CBC) records. Using a dataset comprised of CBC records of 86 ALL and 86 control patients respectively, we identified the most ALL-specific parameters using a feature selection approach. Next, Grid Search-based hyperparameter tuning with a five-fold cross-validation scheme was adopted to build classifiers using Random Forest, XGBoost, and Decision Tree algorithms. A comparison between the performances of the three models demonstrates that Decision Tree classifier outperformed XGBoost and Random Forest algorithms in ALL detection using CBC-based records.

Authors

  • Yousra El Alaoui
    College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar.
  • Regina Padmanabhan
    The Department of Electrical Engineering, Qatar University, Qatar. Electronic address: regina.ajith@qu.edu.qa.
  • Adel Elomri
    College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar.
  • Marwa K Qaraqe
    College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
  • Halima El Omri
    Medical Oncology-Hematology Department, National Centre for Cancer Care and Research (NCCCR), Hamad Medical Corporation (HMC), Doha, Qatar.
  • Ruba Yasin Taha
    Medical Oncology-Hematology Department, National Centre for Cancer Care and Research (NCCCR), Hamad Medical Corporation (HMC), Doha, Qatar.

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