A Risk-to-Action Framework for Questionnaire-Based Lung Cancer Risk Assessment and Clinical Recommendation

Abstract

Lung cancer remains one of the leading causes of cancer-related mortality worldwide, primarily due to delayed diagnosis and limited availability of early screening services. Early identification of warning symptoms and modifiable risk factors can substantially improve clinical outcomes and timely intervention. This study presents a questionnaire-based intelligent system for preliminary lung cancer risk assessment and clinical recommendation. The proposed mobile application collects user information related to demographic characteristics, smoking history, environmental exposure, lifestyle habits, medical history, and common respiratory symptoms such as persistent cough, chest pain, wheezing, and shortness of breath. A weighted rule-based scoring model is applied to evaluate cumulative risk and classify users into Low, Moderate, or High Risk categories. Based on the identified risk level, the system generates personalized health guidance and consultation recommendations. In addition, an integrated AI chatbot provides instant responses to common health-related queries and promotes user awareness regarding lung cancer symptoms and prevention. The proposed framework offers a non-invasive, low-cost, user-friendly, and accessible solution for early-stage risk screening, particularly in rural and resource-constrained regions.