Cancer Disease Investigation and Future Prevention Implications

Abstract

In this study, relative to various numerical iteration techniques, the simulation aligns with PDE (Partial Differential Equation) and discretization applications. From the literature, numerical analysis and parallel performance measurements have been investigated, especially those that involve parameters such as temporal performance, effectiveness, efficiency, speedup, and execution time. However, to visualize breast cancer, the procedure calls for expensive calculations, as well as a huge memory. In the majority of the previous investigations that have focused on breast cancer growth, they have reported the need for parallel computer systems development’s distributed processors, as well as the distributed memory. Therefore, when parallel repository systems are developed based on important computation platforms, there tends to be benefits such as decreased costs and increased speed. In this study, it was established that the proposed simulation software exhibits several strengths. They included being friendly, multidimensional breast cancer visualization, and high performance estimation. Also, the software, in the wake of big data dominance in the healthcare sector, offers strength and real-time solution. The implication is that the proposed model would offer promising results regarding computer-aided decision-making relative to the screening for breast cancer, as well as disease treatment, clinical assessments, and diagnosis.