Publication Date: 2023/06/14
Abstract: Lung cancer is the expansion of malignant cells in the lungs. Due to the rising frequency of cancer, both the death rate for men and women has increased. Lung cancer is a condition in which lung cells proliferate uncontrolled. Although lung cancer cannot be averted, the risk can be decreased. Therefore, early identification of lung cancer is essential for improving patient survival. Lung cancer incidence is directly inversely correlated with the frequency of heavy smokers. Various classification techniques, including Naive Bayes, Random forest, Logistic Regression, Knn, Kernal svm and Artificial neural network were used to investigate the lung cancer prediction. The primary goal of this study is to investigate the effectiveness of classification algorithms and neaural network in the early identification of lung cancer.
Keywords: Naive Bayes, Random Forest, Logistic Regression, Knn , Kernal svm, Artificial Neaural Network ,Machine Learning, Lung Cancer.
DOI: https://doi.org/10.5281/zenodo.8037653
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT23JUN281.pdf
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