Modeling the Factors Associated with Mortality in Patients with Breast Cancer: A Machine Learning Approach

Abstract

Background: Breast cancer (BC) was the fifth leading cause of death worldwide in 2015 and the second leading cause of death in Iran in 2012. This study aimed to model the factors associated with mortality in patients with BC utilizing the machine learning approach.Methods: We used data of patients with primary BC during 2007-2016 in Tabriz, Iran. The data were analyzed using decision tree (DT), boosted tree (BT), random forest (RF), k-nearest neighbors (KNN) and generalized additive model (GAM) with inverse probability of censoring weighting (IPCW) technique to assess the risk factors of mortality. The models were compared by using diagnostic accuracy measures.Results: Accuracy of the models ranged from 76.0 to 93.0%, with sensitivity of 82.5-98.8% and specificity of 72.2-99.4%. The GAM fit the data best with accuracy of 93.0% (95% CI: [90.5, 95.0]), sensitivity of 98.8% (95% CI: [96.9, 99.7]) and specificity of 84.3% (95% CI: [78.8, 88.9]) where non-linear effect of age (p-value = 0.006), grade (p-value = 0.024) and time to event (p-value < 0.001) on mortality were significant. Conclusion: The GAM seems to be an optimal model for classifying the mortality in patients with BC. Considering the time to event, age and grade, as the prognostic factors obtained by GAM, more accurate prevention planning may be designed.