Lung Cancer Prediction Using DBSMOTE and SVM

Lung cancer is one of the leading causes of death nowadays. It has been found in older and younger people in recent years, and the figures are worrying. Detecting lung cancer in the early stage significantly increases the chances of survival. This research proposes a novel hybrid method for early lung cancer diagnosis. This approach uses the Tomek links method, the DBSMOTE algorithm for preprocessing, PCA for feature reduction, and the SVM algorithm for classification. Moreover, we analyzed five classification algorithms on the lung dataset: SVM, KNN, Naïve Bayes, random forest, and Rpart. We compared the classification results with the proposed approach in terms of precision, F1, recall, accuracy, and balance accuracy. The experimental results demonstrated that the proposed method attains the highest classification accuracy (96.72%) than other methods used for the experimental study.