Numerical investigation for breast cancer risk model: An ODE based approach integrating body mass dynamics

Numerical investigation for breast cancer risk model: An ODE based approach integrating body mass dynamics

Breast cancer remains a leading cause of mortality among women worldwide, driven by a complex interplay of genetic, life¬style, and physiological factors. Traditional risk assessment models rely on statistical parameters, limiting their predictive accuracy for breast cancer. This study aims to enhance predictive modeling by applying mathematical approaches, including ordinary dif¬ferential equations (ODE’s) based breast cancer risk model (BCRM), to understand the dynamics of body mass and its impact on cancer risk. We create sufficient large datasets to explore model robustness using methods like Adams’numerical solver, backward differentiation formula (BDF) method, explicit Runge-Kutta technique, and implicit Runge-Kutta method. The results are analyzed by comparing these four state-of-the-art numerical methods. Our findings highlight the strengths of these numerical methods, pre¬senting solution plots and absolute error analyses to demonstrate the efficacy of the breast cancer risk model in capturing cancer risk trajectories and advancing diagnostic accuracy.