Algorithm for Constructing a Classifier Team Using a Modified PCA (Principal Component Analysis) in the Task of Diagnosis of Acute Lymphocytic Leukaemia Type B-CLL

Abstract

Systems of data recognition and data classification are getting more and more developed. There appear newer algorithms that solve more difficult and complex decision problems. Very good results are obtained using sets of classifiers. The authors in their research focused on certain data characteristics. The characteristics concerns recognition of classes of objects whose features can be grouped. Clusters created in this manner can contribute to better recognition of certain decision classes. One such example is a diagnosis of forecast in the case of acute lymphocytic chronic leukaemia B-CLL type. In this document, the authors present a modified selection method of features of the PCA object. The modification concerns the rotation of objects in relation to decision classes. In addition to grouping similar features using Varimax rotation, a procedure for grouping patients in these PCA groups was developed. Within each PCA, two classifiers - strong and weak ones were built. In the research part, the developed method was compared to the one-stage recognition algorithms known from the literature. The obtained results have a significant contribution to medical diagnostics. They allow to develop a procedure for treatment of B-CLL lymphocytic leukaemia. Making an appropriate diagnosis allows to increase a patient’s survival chance by implementing appropriate treatment.