Lung Cancer Prognosis: A Machine Learning Approach to Symptom-Based Prediction and Early Detection

This research facilitates the important work of early detection and prognosis of lung cancer and addresses the great global problem of the growing number of patients affected by this disease. Using a machine learning framework and Random Forest Classifier, the study aims to improve prediction accuracy, especially for high-risk groups. The study was based on a dataset of 1 k records that included factors related to age, race, air pollution, and other health indicators. The objective of this study aligns with the urgency to address lung cancer which is one of the foremost causes of cancer-related morbidity globally. Exploratory data analysis (EDA) plays a foundational role in the methodology. EDA techniques enable to identification and analysis of vital variables that influence lung cancer progression. We perform correlation analysis and assess component importance to derive informed insights. Among the prominent factors influencing lung cancer, the EDA findings pinpoint the detrimental impact of smoking (with a 24.6% prevalence). Certain behaviors, dust allergy (10.3%), and alcohol consumption (6.5%) are also implicated in disease development. This underscores the multifaceted nature of lung cancer and highlights the critical role of environmental and lifestyle factors. This comprehensive strategy presents predictive models as an effective tool in the ongoing fight against lung cancer, significantly improving clinical decisions and the overall management of this health problem.