Machine learning algorithm for lung cancer classification using ADASYN with standard random forest

Lung cancer is one type of cancer that develops in the lungs. Early identification of lung cancer symptoms may lead to a successful treatment. The dataset indicates the presence of duplicate characteristics, as well as an imbalanced classification, making lung cancer classification a challenging task. This study presents a novel approach that combines the ADASYN with the standard random forest (ASRF) model to enhance the efficacy of lung cancer dataset identification. The ASRF, as described, offers interpretable outcomes by using feature significance, hence providing significant insights into the aspects that contribute to judgments on the classification of lung cancer. The classification algorithm is used to ascertain the existence or absence of lung cancer in a certain patient. When comparing the proposed ASRF with the current SVM, MLP, RF and GB, compared to other existing methods, the ASRF technique achieved 93.5% precision, 94.7% recall, 94.1% F-measure, and 94% accuracy.