Statistical analysis of mammographic features and its classification using support vector machine

Muthu Rama Krishnan Mookiah, S. Banerjee, Chinmay Chakraborty, Chandan Chakraborty, A.K. Ray

Research output: Contribution to journalArticlepeer-review

59 Citations (Scopus)


This study aims at designing a support vector machine (SVM)-based classifier for breast cancer detection with higher degree of accuracy. It introduces a best possible training scheme of the features extracted from the mammogram, by first selecting the kernel function and then choosing a suitable training-test partition. Prior to classification, detailed statistical analysis viz., test of significance, density estimation have been performed for identifying discriminating power of the features in between malignant and benign classes. A comparative study has been performed in respect to diagnostic measures viz., confusion matrix, sensitivity and specificity. Here we have considered two data sets from UCI machine learning database having nine and ten dimensional feature spaces for classification. Furthermore, the overall classification accuracy obtained by using the proposed classification strategy is 99.385% for dataset-I and 93.726% for dataset-II, respectively.
Original languageEnglish
Pages (from-to)470-478
Number of pages9
JournalExpert Systems with Applications
Issue number1
Publication statusPublished - Jan 2010


  • Mammogram based data
  • Statistical analysis
  • Support vector machine
  • Kernel function
  • Diagnostic measures


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