Multi-Model Strategy for Precision Lung Cancer Diagnosis using CNN-Enhanced Feature Extraction and Ensemble Learning

Cancer remains one among the deadliest syndromes, the mortality rate of which is as high as 146.0 per 100,000 people per year. These are lung cancer of which a lot has been said. Lung cancer is often not easy to diagnose until later stages, yet symptoms may include long-term coughing, changes in the tone of the voice and tiredness. Lung cancer continues to be one of the primary causes of cancer-related deaths globally, which makes accurate and timely models predicting the disease critical. This research employs machine learning approaches to solve this urgent problem. A variety of more sophisticated algorithms are applied in the study, including, Random Forest, Gradient Boosting, and to enhance patients’ well-being as well as facilitate timely treatments the project would like to improve the lung cancer prediction's precision and understandability.