Breast cancer, mortality prediction, logistic regression
Abstract
The purpose of this study is to develop a statistical model capable of predicting mortality in breast cancer patients based on a comprehensive set of demographic and clinical attributes. Data were analyzed using logistic regression to assess variables such as age, menopausal status and type of treatment. This approach helps to observe the interactions between these variables and their impact on survival outcomes. While logistic regression is effective for understanding linear relationships between variables, it has notable limitations when dealing with the complex, nonlinear nature of breast cancer progression. As a result, this model might not completely capture all aspects of patient prognosis. More advanced statistical techniques are needed to improve the accuracy of predictions in future studies. This strategy could potentially enhance the efficiency of clinical decision making, by allowing for better and more accurate predictions regarding which patients will die and guiding personalized treatment strategies. This strategy would provide an important advancement in both risk stratification and individualized intervention for patients with breast cancer.
