Integrating domain knowledge in supervised machine learning to assess the risk of breast cancer Article

Bochare, A, Gangopadhyay, A, Yesha, Y et al. (2014). Integrating domain knowledge in supervised machine learning to assess the risk of breast cancer . 6(2), 87-99. 10.1504/IJMEI.2014.060245

cited authors

  • Bochare, A; Gangopadhyay, A; Yesha, Y; Joshi, A; Yesha, Y; Brady, M; Grasso, MA; Rishe, N

fiu authors

abstract

  • We used various supervised machine learning and data mining techniques to generate a model for predicting risk of breast cancer in post menopausal women using genomic data, family history, and age. In this paper, we propose an approach to select nine best SNPs using various feature selection algorithms and evaluate binary classifiers performance. We have also designed an algorithm to incorporate domain knowledge into our machine learning model. Our observations revealed that the machine learning model generated using both the domain knowledge and the feature selection technique performed better compared to the naive approach of classification. It is also interesting to note that, in addition to selecting nine best SNPs, feature selection resulted in removing age from the set of features to be used for cancer risk assessment. © Copyright 2014 Inderscience Enterprises Ltd.

publication date

  • January 1, 2014

Digital Object Identifier (DOI)

start page

  • 87

end page

  • 99

volume

  • 6

issue

  • 2