Machine Learning Approach for Predicting Breast Cancer Using Genomic Data

Cancer prediction at an early stage is very crucial as the patient can then prepare for dealing with it. There are several Machine Learning models that help in predicting cancer by identifying samples of independent persons at high risk, facilitating the design and planning of cancer trials. These models use biomarkers like age, menopause, tumor-size, invnodes, breast, breast-quad dimensions to predict breast cancer. However, these models had major drawbacks of late prediction as well as low accuracy. So here presenting the system which uses gene expression profiles (genomic data) to predict breast cancer at an early stage. This model is built using different machine learning algorithms like a highly versatile support vector machine (SVM), Naive Bayes theorem, Decision tree and nearest neighbors approach to predict breast cancer using gene expression profiles.

Keywords: SVM (Support Vector Machine), Naive Bayes theorem, Decision tree, Nearest neighbors, Genomic data

Suggested Citation: Suggested Citation

Sharma, Saurabh and Shah, Neel and Singh, Rishiraj and Lokare, Reena, Machine Learning Approach for Predicting Breast Cancer Using Genomic Data (April 8, 2020). Proceedings of the 3rd International Conference on Advances in Science & Technology (ICAST) 2020, Available at SSRN: https://ssrn.com/abstract=3571724 or http://dx.doi.org/10.2139/ssrn.3571724