Lung Cancer Detection and Classification using Machine Learning Algorithms

Abstract

Lung cancer is a clump of cells in the lung that are multiplying uncontrollably and improperly. Lung cancer is the deadliest disease, and its cure should be the primary focus of all scientific research. Although it cannot be prevented, we can lessen the danger. Thus, a patient's chance of life depends on the early identification of lung cancer. Several machine learning methods, such as Support Vector Machine, Logistic Regression, Artificial Neural Networks, and Naive Bayes, have been used for the investigation and prognosis of lung cancer. In this paper, Lung cancer prediction is finished by gathering the dataset from the survey and applying machine learning methods such as Support Vector Machine, Nave Bayes, K-Nearest Neighbors, Decision Tree, and Random Forest. With this result, it is revealed that Decision Tree attained the maximum accuracy of 100% as compared to the others.