Performance Analysis of Machine Learning Models in Lung Cancer Detection

Lung cancer is one of the most life-threatening diseases, and early identification is key to improving survival rates. Advanced machine learning techniques offer a promising approach for predicting its occurrence. Various models, including Logistic Regression (LR), Random Forest (RF), Gaussian Naïve Bayes (GNB), K-Nearest Neighbors (KNN), Decision Tree (DT), and Support Vector Machine (SVM), were analyzed for their effectiveness in prediction. A dataset containing 309 instances with 16 attributes was utilized for evaluation. Metrics such as accuracy, precision, recall, and F1-score were used to measure performance. Among the models tested, logistic regression demonstrated the highest predictive capability.