ABSTRACT
Background: Various indices derived from red blood cell (RBC) parameters have been described for distinguishing betathalassemia minor and other types of hypochromic microcytic anemia. Objective: The study is aimed at investigating thediagnostic reliability of different RBC indices and formulas in differentiation between beta thalassemia minor and othertypes of hypochromic microcytic anemia. Subjects and Methods: This is a cross‐sectional study which was carried out sincefirst of Jan 2011 to end of December 2011 on 171 children with hypochromic microcytic anemia in Kut Oncology Centre,Wasit, Iraq. Results: There was a statistical significant difference between thalassemic group and other groups regardingblood indices as well as the eight formulas which were used. The highest correctly identified patients (PCIP) was reportedfor RBCs count (84%) with sensitivity and specificity of 96.3%. The Youden's index for RBCs was 58.2 which is the highestvalue compared with other seven parameters or indices which were used in this study. The second highest Youden's indexwas for G & K index, with 78.4% PCIP, and sensitivity and specificity of 98.2%. Youden's index of red cell distributionwidth (RDW) was the lowest value compared to other values used in this study as well as the lowest percentage of correctlyidentified patients (65%). The sensitivity and specificity of RDW for BTM was 86.1%. Conclusion: According to this study,cell counter-based parameters and formulas, particularly RBCs, and Green and King index are superior to all othermethods examined for distinguishing between thalassemia trait and other hypochromic microcytic anemia; while, RDW wasinadequate and ineffective for that purpose.
ABSTRACT
This paper proposes a novel P1-weighted Lukasiewicz Logic based Fuzzy Similarity Classifier for classifying Denver Group of chromosomes and compares its performance with the other classifiers under study. A chromosome is classified to one of the seven groups from A to G, based on the Denver System of classification of chromosomes. Chromosomes within a particular Denver Group are difficult to identify, possessing almost identical characteristics for the extracted features. This work evaluates the performance of supervised classifiers including Naive Bayes, Support Vector Machine with Gaussian Kernel (SVM), Multilayer perceptron (MLP) and a novel, unsupervised, P1-weighted Lukasiewicz Logic based Fuzzy Similarity Classifier, in classifying the Denver Group of chromosomes. A fundamental review on fuzzy similarity based classification is presented. Experimental results clearly demonstrates that the proposed P1-weighted Lukasiewicz Logic based Fuzzy Similarity Classifier using the generalized Minkowski mean metric, produces the best classification results, almost identical to the Ground Truth values. One-way Analysis of Variance (ANOVA) at 95% and 99% level of confidence and Tukey's post-hoc analysis is performed to validate the selection of the classifier. The proposed P1-weighted Lukasiewicz Logic based Fuzzy Similarity Classifier gives the most promising classification results and can be applied to any large scale biomedical data and other applications.
Este trabalho propõe uma nova lógica P1pondera de Lukasiewicz de acordo com o classificador de similarida fuzzy para classificar cromossomas do Grupo Denver e compara o seu desempenho com os outros classificadores em estudo. Um cromossoma é classificado com um dos sete grupos de A a G, com base no Sistema de Denver de classificação de cromossomos. Cromossomos dentro de um grupo de Denver particular são difíceis de identificar, com características quase idênticas para os recursos extraídos. Este trabalho avalia o desempenho de classificadores supervisionados, incluindo Naive Bayes, Support Vector Machine com Gaussian Kernel (SVM), perceptron multicamadas (MLP) e um novo classificador sem supervisão, P1-weighted, lógica de Lukasiewicz de acordo com o classificador de similaridade Fuzzy para a classificação do Grupo Denver de cromossomos . Apresenta-se ma revisão fundamentada na classificação de acordo com similaridade difusa. Resultados experimentais demonstram claramente que Classificador Similaridade Fuzzy proposto de acordo com a lógica de Lukasiewicz P1-weighted usando a médica métrica de Minkowski para produz melhores resultados de classificação. Estes valores foram muito similares aos valores de Ground Truth . Análise de variancia (ANOVA) com 95% de grau de confiança e análise post-hoc de Tukey 99% foram realizadas para validar a seleção do classificador. Este classificador P1-weighted de lógica de Lukasiewicz está de acordo com o classificador de similaridade difusa oferecendo resultados declassificação mais promissoras. Portanto, podendo ser aplicado a dados biomédicos em larga escala além de outras aplicações.