Compartmental and Probabilistic Modelling for Research into the Development of Cancer and its Biological History

Abstract

Compartmental models are frequently employed to forecast cancer progression and potential treatment planning. The differential equation governs the cancer model, which is explored along with other mathematical models for cancer such as the exponential, linear, surface, logistic, Gompertz, Mendelsohn, and Bertanffy models. Also, a numerical illustration of the above models has been discussed. The results of the cancer model are beneficial for creating new treatment plans. The Bayes theorem is used in this study's probabilities model to determine the optimum course of treatment for cancer patients. The built-in mathematical model is subjected to a variety of theoretical operations, particularly for the development of new clinical medications. New therapeutic therapies receive specific instruction, and appropriate variables and parameters are also examined. Parallel to this, the model presents crucial construction challenges.