College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar.
Medical Oncology-Hematology Department, National Centre for Cancer Care and Research (NCCCR), Hamad Medical Corporation (HMC), Doha, Qatar.
Stud Health Technol Inform. 2023 Jun 29;305:265-268. doi: 10.3233/SHTI230479.
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.
本研究提出了一种新颖的急性淋巴细胞白血病（ALL）诊断模型，仅基于全血细胞计数（CBC）记录。我们使用包含 86 例 ALL 患者和 86 例对照患者的 CBC 记录数据集，通过特征选择方法确定了最具 ALL 特异性的参数。接下来，我们采用基于网格搜索的超参数调优方法，并结合五重交叉验证方案，使用随机森林、XGBoost 和决策树算法构建分类器。通过比较这三种模型的性能，我们发现基于 CBC 记录的 ALL 检测中，决策树分类器的性能优于 XGBoost 和随机森林算法。
