Lung Cancer Risk Prediction with Machine Learning

Lung Cancer Risk Prediction with Machine Learning

Authors- Rafael Jernaldin Raj , Assistant Professor Dr. Lipsa Nayak

Abstract--Lung cancer remains a major contributor to cancer-related mortality worldwide, highlighting the critical need for early detection and accurate risk prediction. This project introduces a machine learning-based lung cancer risk prediction system that analyzes clinical, demographic, and imaging data to deliver real-time, accurate assessments. Using supervised learning techniques like Random Forest and XGBoost for structured data, and convolutional neural networks (CNNs) for medical imaging, the system offers a comprehensive evaluation of a patient’s cancer risk. With automated data processing, explainability tools like SHAP and LIME, and integration with electronic health records (EHRs), the system enhances clinical decision-making and promotes timely interventions.

DOI: /10.61463/ijset.vol.13.issue2.438+