EFFICIENT MACHINE LEARNING MODEL TO IDENTIFY THE LUNG CANCER USING DYNAMIC FEATURE EXTRACTION

Abstract

An estimated 1.2 million people were diagnosed with lung cancer in 2000, making it the most frequent disease worldwide (12.3% of all malignancies). Cigarette smokers are responsible for 80% to 90% of lung cancers. In both sexes, lung cancer continues to be the major cause of cancer-related death in the United States and elsewhere. Tobacco use and smoking are responsible for nearly all occurrences of lung cancer. Other causes of lung cancer include exposure to radon gas, asbestos, air pollution, and persistent infections. Further, many potential risk factors for developing lung cancer have been proposed, including both genetic and environmental factors. Small-cell lung carcinomas (SCLC) and non-small-cell lung carcinomas (NSCLC) are the two main histologic subtypes of lung cancer and exhibit distinct patterns of growth and metastasis (NSCLC). Surgery, radiation treatment, chemotherapy, and targeted therapy are all viable alternatives for treating lung cancer. Different characteristics, such as the nature and extent of the malignancy, inform suggestions for treatment approaches. A diagnosis of lung cancer at an early stage can save the lives of patients. Several machine learning algorithms were used to make lung cancer forecasts in this study.