A Data-Centric Method to Identify Lung Cancer Using a Variety of Machine Learning Approaches

Lung cancer continues to be the leading cause of cancer-related deaths worldwide, and early detection is essential to extending patient life expectancy. The goal of this study is to boost the precision of lung cancer diagnosis through the use of modern machine learning approaches. We developed a thorough data pretreatment strategy that included feature selection, encoding, oversampling, and hyper parameter tuning to handle data discrepancies. We used the Kaggle dataset to assess six ML algorithms: Naive Bayes, Decision Tree, Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN), and Logistic Regression. With an accuracy of 99.38%, precision of 99.39%, recall of 99.38%, and F1 score of 99.38%, the SVM approach performed exceptionally well.