• data classification;
  • lymphocytic leukemia;
  • support vector machines;
  • white blood cells


This study introduces a new algorithm to optimize the pattern recognition of different white blood cell types in flow cytometry. The behavior of parametric data clusters in a multidimensional space is analyzed using the learning system known as Support Vector Machines (SVM). Beckman-Coulter Corporation supplied flow cytometry data of numerous patients to be used as training and testing sets for the algorithm. Subsequently, the characteristics of the cells provided in these sets were used to train a SVM based classifier. The objective in developing this algorithm was to identify the category of a given blood sample and provide information to medical doctors in the form of diagnostic references for a specific disease state, lymphocytic leukemia. With the application of the hypothesis space, the learning bias and the learning algorithm, the SVM classifier was successfully trained to evaluate misclassification ratios in flow cytometry data in an effort to recognize abnormal blood cell patterns and address the ubiquitous problem of data overlap through the use of the maximal margin classifier.