Lung Cancer Prediction Using Machine Learning: A Comparative study of Classification Algorithms

Lung cancer is a leading cause of death globally, often diagnosed at advanced stages due to the lack of early symptoms. Early detection is crucial for improving treatment outcomes and survival rates. With rising global cases, predicting lung cancer before it becomes critical is a key public health challenge. Symptoms like persistent cough, shortness of breath, and weight loss can signal the disease but are also common in other conditions, making early prediction vital for timely intervention. This study compares K-Nearest Neighbors (KNN), Random Forest, and Gaussian Naive Bayes (GNB) for lung cancer prediction. KNN and Random Forest showed extremely high accuracy, with KNN reaching 0.87. In contrast, GNB performed slightly worse. In addition, this paper also conducted feature importance analysis to explore the most critical feature factors for lung cancer prediction. The results highlight the effectiveness of machine learning in early lung cancer detection and provide a feature importance analysis, suggesting that better management of the disease can ultimately lead to improved outcomes for patients.