Robust Detection of Chronic Lymphocytic Leukemia with Support Vector Machines and Flow Cytometry

Abstract Aim: Our aim is to build a precise automatic tool for the diagnosis of CLL with the help of machine learning algorithms and flow cytometry immunophenotypic data. Methods: We run experiments with two machine learning methods. First one is decision tree which was previously used in other similar works and second one is support vector machines which is considered to be a more robust classification method. Results : Among the 40 CLL patients from the test set, the model correctly predicts 38 of them and among the 20 other B-CLPD patients, the model predicts 18 of them correctly. Its sensitivity, which is the fraction of true positive predictions among all positive samples, is 95% (38/40). Conclusion : The model achieves very high accuracies on our leave out test set. This model can be a useful tool for automatic CLL diagnosis.