• Hamid Reza Marateb, Marjan Mansourian, Peyman Adibi, and Dario Farina.
• Department of Biomedical Engineering, Engineering Faculty, the University of Isfahan, Isfahan, Iran.
• J Res Med Sci. 2014 Jan 1; 19 (1): 47-56.

Backgroundselecting the correct statistical test and data mining method depends highly on the measurement scale of data, type of variables, and purpose of the analysis. Different measurement scales are studied in details and statistical comparison, modeling, and data mining methods are studied based upon using several medical examples. We have presented two ordinal-variables clustering examples, as more challenging variable in analysis, using Wisconsin Breast Cancer Data (WBCD).Ordinal To Interval Scale Conversion Examplea breast cancer database of nine 10-level ordinal variables for 683 patients was analyzed by two ordinal-scale clustering methods. The performance of the clustering methods was assessed by comparison with the gold standard groups of malignant and benign cases that had been identified by clinical tests.Resultsthe sensitivity and accuracy of the two clustering methods were 98% and 96%, respectively. Their specificity was comparable.Conclusionby using appropriate clustering algorithm based on the measurement scale of the variables in the study, high performance is granted. Moreover, descriptive and inferential statistics in addition to modeling approach must be selected based on the scale of the variables.

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