Application of Bayesian Methods in Cancer Prediction

According to the "Global Cancer Statistics 2020" report released by the International Agency for Research on Cancer, there were approximately 19.3 million new cancer cases worldwide in 2020, and nearly 10 million cancer-related deaths. These figures indicate that cancer has emerged as one of the major public health challenges globally, characterized by extremely high incidence and mortality rates. Cancer is particularly daunting because many forms of the disease exhibit no obvious symptoms in their early stages, often being diagnosed only at advanced stages, thereby complicating treatment efforts. In this context, early prediction of cancer becomes critically important as it enables precision treatment, reduces the overall burden of cancer therapy, alleviates the physical and psychological suffering of patients, and lessens the economic strain on their families, ultimately diminishing the threat posed by cancer to human society. This review systematically examines the use of Bayesian methods in cancer prediction. The paper outlines various Bayesian classification techniques and discusses their unique approaches to handling high-dimensional clinical and molecular data.