Adding features from the mathematical model of breast cancer to predict the tumour size. Issue 3 (2nd July 2020)

• Record Type: Journal Article
• Authors: Nave, OPhir
• Is Part Of: International journal of computer mathematics. Volume 5:Issue 3(2020)
• Page Start: 159
• Page End: 174
• Publication Date: 2020-07-02
• Subjects: Mathematical model -- Breast cancer -- Machine learning -- Theoretical biology; 34A34; Computer systems -- Periodicals; Computer systems; Periodicals; 004
• DOI: 10.1080/23799927.2020.1792552
• Languages: English
• ISSNs: 2379-9927

Abstract

Abstract : In this study, we combine a theoretical mathematical model with machine learning ( ML ) to predict tumour sizes in breast cancer. Our study is based on clinical data from 1869 women of various ages with breast cancer. To accurately predict tumour size for each woman individually, we solved our customized mathematical model for each woman, then added the solution vector of the dynamic variables in the model (in machine learning language, these are called features) to the clinical data and used a variety of machine learning algorithms. We compared the results obtained with and without the mathematical model and showed that by adding specific features from the mathematical model we were able to better predict tumour size for each woman.