Early Lung Cancer Analysis and Prediction Using Machine Learning Classification Techniques

Abstract

 Lung cancer is one of the leading causes of death worldwide and early detection is important for improving patient survival. Lung cancer develops due to the abnormal growth of cells in the lungs. This study applies machine learning classification algorithms to detect lung cancer accurately and efficiently. Three algorithms, namely K-Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Machine (SVM), are used to classify lung cancer cases. The performance of these models is evaluated using accuracy, sensitivity, specificity, and Area Under the Curve (AUC). The experimental results show that machine learning techniques can support early lung cancer detection and assist healthcare professionals in making reliable diagnostic decisions.