An Analysis On Cancer Data Using Mathematical Modelling

There has been an ongoing push to employ data-driven methods to better understand, diagnose, and treat cancer as it is still one of the top killers globally. In order to improve forecast accuracy and find meaningful patterns in cancer data, this research offers a mathematical modelling approach. A thorough dataset was created for analysis by collecting clinical data from hospital visits and supplementing it with secondary sources. Following data preprocessing, a model reflecting the illness's course and features was developed using relevant mathematical methods. Subsequently, the model's prediction power and applicability to actual cancer dynamics were evaluated by validation. The results show that mathematical modelling may be a useful tool for cancer research, providing information that can help with policymaking and clinical decision-making.