The Modified Principal Component Analysis Feature Extraction Method for the Task of Diagnosing Chronic Lymphocytic Leukemia Type B-CLL

Abstract

The vast majority of medical problems are characterised by the relatively high spatial dimensionality of the task, which becomes problematic for many classic pattern recognition algorithms due to the well-known phenomenon of the curse of dimensionality. This creates the need to develop methods of space reduction, divided into strategies for the selection and extraction of features. The most commonly used tool of the second group is the PCA, which, unlike selection methods, does not select a subset of the original set of features and performs its mathematical transformation into a less dimensional form. However, natural downside of this algorithm is the fact that class context is not present in supervised learning tasks. This work proposes a feature extraction algorithm using the approach of the pca method, trying not only to reduce the feature space, but also trying to separate the class distributions in the available learning set. The problematic issue of the work was the creation of a method of feature extraction describing the prognosis for a chronic lymphocytic leukemia type B-CLL, which will be at least as good, or even better than when compared to other quality extractions. The purpose of the research was accomplished for binary and three-class cases in the event in which for verification of extraction quality, five algorithms of machine learning were applied. The obtained results were compared with the application of paired samples Wilcoxon test.

The impact of the COVID-19 pandemic on the mental health of children with psychiatric diagnoses – multidimensional CCPCA Model

“…Another method, Linear Discriminant Analysis (LDA), provides for partition into regions with linear functions [ 60 ]. Finally, the last two methods used for modelling were (i) Class-Centroid Principal Component Analysis (CCPCA), which involves the rotation of factors according to class centroids [ 61 – 63 ], and (ii) Gradient Principal Component Analysis (GPCA), in which stochastic gradient is applied to estimate the best rotation angle and the search step length [ 64 ].…”

Generation Z in the Labour Market - Contestation or Adaptation?

“…For each question, the compatibility of distribution was calculated with the chi-squared compatibility test. Subsequently, in order to determine the strength of discrimination of given answers against others within a given question (indication of those answers that are statistically dominant in comparison with others at the level of statistical significance p<0.05), CCPCA (Centroid Class Principal Component Analysis) and GPCA (Gradient Class Principal Component Analysis) were carried out (Topolski, 2020a;2020b).…”

The Value of Work in the Life of Representants of Generation Z – Autotelic or Instrumental?

“…For each question, the compatibility of the distribution with the uniform distribution was calculated by the chi-squared compatibility test. This was followed by CCPCA (Topolski, 2020a) and GPCA (Topolski, 2020b) analyses in order to determine the strength of discrimination between the answers in question and other answers within the question. The analyses allowed to obtain answers which, compared to others, are statistically dominant significantly at the level of statistical significance of p<0.05.…”