%0 Journal Article
%T Classifying Y-Short Tandem Repeat Data: A Decision Tree Approach
%A Ali Seman
%A Azizian Mohd Sapawi
%A Ida Rosmini Othman2
%A Zainab Abu Bakar
%J Open Journal of Immunology
%D 2013
%R 10.4172/jpb.1000290
%X Classifying Y-Short Tandem Repeat data has recently been introduced in supervised and unsupervised classifications. This study continues the efforts in classifying YSTR data based on four decision tree models: CHisquared Automatic Interaction Detection (CHAID), Classification and Regression Tree (CART), Quick, Unbiased, Efficient Statistical Tree (QUEST) and C5. A data mining tool, called IBM Statistical Package for the Science Social Modeler 15.0 (IBM£¿ SPSS£¿ Modeler 15) was used for evaluating the performances of the models over six Y-STR data. Overall results showed that the decision tree models were able to classify all six Y-STR data significantly. Among the four models, C5 is the most consistent modelm where it had produced the highest accuracy score of 91.85%, sensitivity score of 93.69% and specificity score of 96.32%
%K Ali Seman
%K Ida Rosmini Othman2
%K Azizian Mohd Sapawi and Zainab Abu Bakar
%K Classification
%K Supervised classification
%K Decision Tree
%K Predictive model
%K Y-STR data
%U https://www.longdom.org/abstract/classifying-yshort-tandem-repeat-data-a-decision-tree-approach-33402.html