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Preliminary evaluation of statistical modeling for non-invasive detection of malignancy in the prevalence of breast diseases is discussed. The standard databases for breast cancer symptoms, mammography diagnosing features and breast cancer ultrasound Elastography imaging screening standards were used, with ten dataset features as attributes. The satisfying conditions of the features were categorized for the classification as benign or malignant classes. The interpretation criteria in Elastography consist of the qualitative parameter elasticity score and the quantitative parameter strain ratio. Training of dataset was first done using 180 biopsy cases with 132 benign and 48 malignant results. 95% confidence interval for symptomatic was 1.625 to 4.955; mammographic was 1.506 to 5.494 and ultrasound Elastography imaging was 2.213 to 6.087. The model created was further tested with 210 cases using three machine learning classifiers and results were compared with gold standard biopsy results. Performance characteristics were statistically analyzed. The three classifiers have yielded an accuracy of 95.7%, 84.3% and 91.4% respectively and the statistical models proved its efficiency in differentiating malignant from benign.
Breast cancer, Mammography, Supervised learning, Symptomatic, Ultrasound elastography, #