MATHEMATICAL MODELING OF LUNG CANCER USING ROUGH SETS

Lung tumor is a severe disease, and the study of the disease has vital applications in modeling. This study is a piece of knowledge and information-based approach to support human decision-making, learning, and activity. The Rough set method is the most valuable mathematical method to deal with imperfect learning and uncertainty and a robust system for the patterns of life expectancy in lung tumors. Many researchers have put forward their conclusions in the research of tumors, but the patterns of life expectancy in lung tumors have not been seen yet using the Rough set. This paper presents the dynamics of tumor cells' growth with anti-tumor treatment. It deals with the applications of the Rough set method in the patterns of life expectancy in lung tumors in uncertain situations through information knowledge and dataintensive computer-based solutions. This realistic clinical data evaluation strategy shows that the system performance accuracy for the pattern of life expectancy in lung tumors is 98.00 % by the Rough set method, whereas the accuracy found in other ways (ANN, Boosted SVM) was less in the previous studies.