Lung Cancer Disease Detection Using Machine Learning

Lung cancer is the leading cause of cancer-related deaths globally, with approximately 1.8 million fatalities each year. The low five-year survival rate (under 20%) is largely due to late-stage diagnosis. However, early detection can significantly increase survival rates to over 90%. Machine learning (ML) offers a promising solution by identifying subtle patterns in medical data that traditional diagnostic methods often miss. This study offers a thorough evaluation of machine learning (ML) techniques for detecting lung cancer. We examined a wide array of algorithms, ranging from traditional classifiers like Support Vector Machines (SVM) and Random Forests (RF) to advanced deep learning architectures such as Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN). Our experimental framework utilized publicly available datasets, including LIDC-IDRI, NSCLC Radiomics, and various Kaggle repositories. To ensure high data quality and model robustness, we applied rigorous preprocessing steps (normalization, augmentation, noise reduction) and feature selection methods (PCA, Recursive Feature Elimination). Our comparative analysis revealed CNNs as the top performer, achieving 95% accuracy, 94% precision, and an AUC-ROC of 96%. Traditional ML models also demonstrated strong performance (RF: 88% accuracy; SVM: 85% accuracy), highlighting their continued utility in environments with limited resources. We validated our findings by presenting detailed performance metrics, ROC curves, and confusion matrices. Beyond performance metrics, we also addressed significant challenges to clinical implementation, including interpretability (using Grad-CAM and SHAP), data scarcity, ethical considerations, and regulatory compliance. We discussed emerging trends like explainable AI, federated learning for data privacy, and the multi-modal integration of imaging with electronic health records as potential avenues for practical application. Ultimately, our work provides valuable insights for both researchers and clinicians, demonstrating how ML can revolutionize lung cancer screening, leading to earlier diagnoses, optimized treatment plans, and improved patient outcomes.