Abstract
Breast cancer (BC) is a prevalent disease that contributes significantly to female mortality worldwide, particularly among young women, who often present with aggressive tumor. Despite the need for accurate prognosis in this demographic, existing studies have focused on broader age groups and often rely on the SEER database, which has limitations in variable selection. Data from 3,401 patients with BC were obtained from the Breast Cancer Public Staging Database. Patients were categorized as younger (n = 1,574) and older (n = 1,827). We utilized various survival models—Random Survival Forest, Gradient Boosting Survival, Extra Survival Trees (EST), and two penalized Cox proportional hazards models, Lasso and ElasticNet—to analyze and compare BC mortality characteristics between the groups. Additionally, older patients exhibited a higher prevalence of comorbidities compared to younger patients. The EST model outperformed the other models in predicting mortality for both age groups. Tumor stage was the primary variable used to train the model for mortality prediction in both groups. COPD was a significant variable only in younger patients with BC. Other variables exhibited varying degrees of consistency in each group. These findings can help identify high-risk young female patients with BC who require aggressive treatment by predicting the risk of mortality.