Smart Lung Cancer Prediction Using Machine Learning Algorithms

Abstract

Lung cancer is a commonly diagnosed type of cancer. It is a highly life-threatening disease. An accurate prediction of lung cancer can reduce the death rates as accurate prediction can help doctors early in their decision-making to start the treatment of the patients. In this article, three machine learning (ML) algorithms, random forest (RF), support vector machine (SVM), and XGBoost, are utilized. The proposed model’s performance was evaluated using a confusion matrix. The proposed model achieved a high testing accuracy of 96.77% with the XGBoost algorithm. This study highlights the potential of using ML algorithms to enhance the accuracy of lung cancer prediction.