  Flow cytometry is a technique that measures multiple fluorescence and light
scatter-associated parameters from individual cells as they flow a single file
through an excitation light source. These cells are labeled with antibodies to
detect various antigens and the fluorescence signals reflect antigen
expression. Interpretation of the multiparameter flow cytometry data is
laborious, time-consuming, and expensive. It involves manual interpretation of
cell distribution and pattern recognition on two-dimensional plots by highly
trained medical technologists and pathologists. Using various machine learning
algorithms, we attempted to develop an automated analysis for clinical flow
cytometry cases that would automatically classify normal and chronic
lymphocytic leukemia cases. We achieved the best success with the Gradient
Boosting. The XGBoost classifier achieved a specificity of 1.00 and a
sensitivity of 0.67, a negative predictive value of 0.75, a positive predictive
value of 1.00, and an overall accuracy of 0.83 in prospectively classifying
cases with malignancies.
