ISI Journals

Cancer prevention remains a paramount objective in public health, necessitating innovative approaches to predict, monitor, and mitigate risks. This article explores the integration of finite-volume methods and time series analysis as a novel framework for improving cancer prevention strategies. Finite- volume methods, primarily used in computational fluid dynamics, offer robust capabilities for solving differential equations that describe the spatial and temporal dynamics of cancerous cell growth. Coupling these methods with time series analysis enables the extraction of meaningful patterns and trends from cancer incidence data over time. This study presents a comprehensive literature review, outlines a research methodology combining these techniques, and discusses the results of a case study application. The findings suggest that this integrated approach can significantly enhance the precision and effectiveness of cancer prevention measures.