On the Application of Combine Soft Set with Near Set in Predicting the Lung Cancer Mortality Risk

Abstract

The advancement of artificial intelligence is quick as it can be quickly deployed in many ways, such as medical diagnosis. Lung cancer is both men's and women's deadliest form of cancer. The best clinical approach to non-small resectable cell lung cancer treatment is surgical. Patients who undergo lung cancer thoracic surgery do so with the hope that their lives will be prolonged for a reasonable period afterward. In this paper, we suggest an expert system for calculating the risk factor for mortality one year after thoracic lung cancer surgery. Centered on clinical and functional evidence from cancer patients with lung cancer resections, we are developing an interesting hybrid model combining near sets with soft sets, namely soft near sets. as a system for not only predicting patient lung survival or not but also, to determine the degree of risk. Some fundamental concepts of the proposed model are introduced. Basic properties are deduced and supported with proven propositions. The correct survival classification is done with 90.0 % accuracy. Our innovative soft-near setbased criteria for determining the survival rate is an effective and reliable diagnostic process. Identify the possibility of lung cancer surgery will help the doctor and patients make a more informed decision about how to locate the treatment methods.