Integrating domain knowledge in supervised machine learning to assess the risk of breast cancer Online publication date: Sat, 24-May-2014
by Aniket Bochare; Aryya Gangopadhyay; Yelena Yesha; Anupam Joshi; Yaacov Yesha; Mary Brady; Michael A. Grasso; Napthali Rishe
International Journal of Medical Engineering and Informatics (IJMEI), Vol. 6, No. 2, 2014
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.
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