Cell Population Data (CPD) provides various blood cell parameters that can be used for differential
diagnosis. Data analytics using Machine Learning (ML) have been playing a pivotal role in
revolutionizing medical diagnostics. This research presents a novel approach of using ML algorithms
for screening hematologic malignancies using CPD. The data collection was done at Konkuk University
Medical Center, Seoul. A total of (882 cases: 457 hematologic malignancy and 425 hematologic nonmalignancy)
were used for analysis. In our study, seven machine learning models, i.e., SGD, SVM, RF,
DT, Linear model, Logistic regression, and ANN, were used. In order to measure the performance of our
ML models, stratified 10-fold cross validation was performed, and metrics, such as accuracy, precision,
recall, and AUC were used. We observed outstanding performance by the ANN model as compared
to other ML models. The diagnostic ability of ANN achieved the highest accuracy, precision, recall,
and AUC ± Standard Deviation as follows: 82.8%, 82.8%, 84.9%, and 93.5% ± 2.6 respectively. ANN
algorithm based on CPD appeared to be an efficient aid for clinical laboratory screening of hematologic
malignancies. Our results encourage further work of applying ML to wider field of clinical practice.