One of the large spread diseases in a human being is Lung Cancer. It remains a threat to society and is the cause of thousands of deaths worldwide. Early detection cause of lung cancer is an understandable perspective to maximize the opportunity of the existence of the patients. This paper is about the observation of lung cancer. Here, Computed Tomography (CT) is used for the observation of lung cancer. Various Algorithms are used to search out lung cancer prediction correctly like K Nearest Neighbor, SVM, Decision Tree, and many more. An Aim of the introduced analysis to design a model that can reduce the likelihood of lung cancer in a patient with maximum accuracy. We began by surveying various machine learning techniques, explaining a concise definition of the most normally used classification techniques for identifying lung cancer. Then, we analyze survey representable research works utilizing learning machine classification methods in this field. Moreover, an elaborated comparison table of surveyed paper is introduced.
Gene Expression Analysis for Early Lung Cancer Prediction Using Machine Learning Techniques: An Eco-Genomics Approach
