Artificial immune system features added to breast cancer clinical data for machine learning (ML) applications

We here propose a new method of combining a mathematical model that describes a chemotherapy treatment for breast cancer with a machine-learning (ML) algorithm to increase performance in predicting tumor size using a five-step procedure. The first step involves modeling the chemotherapy treatment protocol using an analytical function. In the second step, the ML algorithm is trained to predict the tumor size based on clinico-pathological data and data obtained from magnetic resonance imaging results at different time points of treatment. In the third step, the model is solved according to adjustments made at the individual patient level based on the initial tumor size. In the fourth step, the important variables are extracted from the mathematical model solutions and inserted as added features. In the final step, we applied various ML algorithms on the merged data. Performance comparison among algorithms showed that the root mean square error of the linear regression decreased with the addition of the mathematical results, and the accuracy of prediction as well as the F1-scores increased with the addition of the mathematical model to the neural network. We established these results for four different cohorts of women at different ages with breast cancer who received chemotherapy treatment.