Prediction of Breast Cancer Biopsy Outcomes – an Approach using Machine Leaning Perspectives
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10.5120/17556-8162 |
Sandeep Chaurasia, Prasun Chakrabarti and Neha Chourasia. Article: Prediction of Breast Cancer Biopsy Outcomes - an Approach using Machine Leaning Perspectives. International Journal of Computer Applications 100(9):29-32, August 2014. Full text available. BibTeX
@article{key:article,
author = {Sandeep Chaurasia and Prasun Chakrabarti and Neha Chourasia},
title = {Article: Prediction of Breast Cancer Biopsy Outcomes - an Approach using Machine Leaning Perspectives},
journal = {International Journal of Computer Applications},
year = {2014},
volume = {100},
number = {9},
pages = {29-32},
month = {August},
note = {Full text available}
}
Abstract
Breast cancer is the most frequently diagnosed cancer in USA. Furthermore breast cancer is the second major cause of death for women in USA. Several well established tools are currently used for screening for breast cancer including clinical breast exam, mammograms and ultrasound. Mammography is one of the most effective in terms of accuracy and cost. However the low positive predicted value (PPV) of breast cancer biopsies resulting from mammograms leads to 70% unnecessary biopsies with benign outcomes. In order to reduce the large number of surgical biopsies of breast, several CAD based system has been proposed in the last decades. Using these systems the radiologist gets an aid on their decision to perform breast biopsies. The dataset used is based on BIRADS findings. Prior work achieves good result with decision tree and neural network. The paper use AutoMLP, BP (back propagation) neural network and support vector machine (SVM) approach to predict the outcomes of mammogram with better result. Using SVM the false biopsies should significantly reduced to only 13%.
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