Comput. Biol. Med. - A classification system based on a new wrapper feature selection algorithm for the diagnosis of primary and secondary polycythemia.

Tópicos

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Resumo

Primary and Secondary Polycythemia are diseases of the bone marrow that affect the blood's composition and prohibit patients from becoming blood donors. Since these diseases may become fatal, their early diagnosis is important. In this paper, a classification system for the diagnosis of Primary and Secondary Polycythemia is proposed. The proposed system classifies input data into three classes; Healthy, Primary Polycythemic (PP) and Secondary Polycythemic (SP) and is implemented using two separate binary classification levels. The first level performs the Healthy/non-Healthy classification and the second level the PP/SP classification. To this end, a novel wrapper feature selection algorithm, called the LM-FM algorithm, is presented in order to maximize the classifier's performance. The algorithm is comprised of two stages that are applied sequentially: the Local Maximization (LM) stage and the Floating Maximization (FM) stage. The LM stage finds the best possible subset of a fixed predefined size, which is then used as an input for the next stage. The FM stage uses a floating size technique to search for an even better solution by varying the initially provided subset size. Then, the Support Vector Machine (SVM) classifier is used for the discrimination of the data at each classification level. The proposed classification system is compared with various well-established feature selection techniques such as the Sequential Floating Forward Selection (SFFS) and the Maximum Output Information (MOI) wrapper schemes, and with standalone classification techniques such as the Multilayer Perceptron (MLP) and SVM classifier. The proposed LM-FM feature selection algorithm combined with the SVM classifier increases the overall performance of the classification system, scoring up to 98.9% overall accuracy at the first classification level and up to 96.6% at the second classification level. Moreover, it provides excellent robustness regardless of the size of the input feature subset used.

Resumo Limpo

primari secondari polycythemia diseas bone marrow affect blood composit prohibit patient becom blood donor sinc diseas may becom fatal earli diagnosi import paper classif system diagnosi primari secondari polycythemia propos propos system classifi input data three class healthi primari polycythem pp secondari polycythem sp implement use two separ binari classif level first level perform healthynonhealthi classif second level ppsp classif end novel wrapper featur select algorithm call lmfm algorithm present order maxim classifi perform algorithm compris two stage appli sequenti local maxim lm stage float maxim fm stage lm stage find best possibl subset fix predefin size use input next stage fm stage use float size techniqu search even better solut vari initi provid subset size support vector machin svm classifi use discrimin data classif level propos classif system compar various wellestablish featur select techniqu sequenti float forward select sffs maximum output inform moi wrapper scheme standalon classif techniqu multilay perceptron mlp svm classifi propos lmfm featur select algorithm combin svm classifi increas overal perform classif system score overal accuraci first classif level second classif level moreov provid excel robust regardless size input featur subset use

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