Abstract-Breast cancer is the most common form of cancer in women. Breast cancer comprises 22.9% of invasive cancers in women and 16% of all the female cancers. Our study takes into consideration data of postmenopausal women of European descent and their single nucleotide polymorphism (SNP) information to assess the risk of developing breast cancer. 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 9 best SNPs using various feature selection algorithms and evaluate binary classifiers performance. We evaluated the performance of binary classifier by adding the domain knowledge of 11 SNPs into the training set and performing classification based on most informative features obtained from feature selection technique. 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 9 best SNPs, feature selection resulted in removing age from the set of features to be used for cancer risk assessment, and the machine learning model generated using both feature selection and domain knowledge provided improved performance without using age for prediction, compared with the naïve method that did use both age and family history among the features. We could observe improvement in the accuracy and sensitivity values using domain knowledge learning which could be beneficial for initial screening.
REF gives a method to integrate domain knowledge in the field of breast cancer risk assessment by adding virtual instances to the original dataset.
Integrating domain knowledge in supervised machine learning to assess the risk of breast cancer
IJMEI
IJMEI
Computer Science