Utilizing Machine Learning to Predict Lung Cancer Severity: Integrating Patient Insights for Improved Clinical Outcomes

Lung cancer remains the leading cause of cancer-related mortality worldwide, necessitating effective predictive tools for improved clinical outcomes. This study employs machine learning (ML) techniques to analyze a comprehensive dataset, focusing on critical risk factors such as smoking habits, air pollution exposure, and demographic information. The objectives are to identify significant risk factors, assess the accuracy of various ML algorithms in predicting lung cancer severity, evaluate the performance of different models, and explore the impact of integrating multi-omics data. The findings reveal that machine learning models, particularly Convolutional Neural Networks and XGBoost, demonstrate high accuracy and predictive capabilities. Integrating multi-omics data further enhances these models, facilitating early intervention and personalized treatment strategies. This research underscores the importance of advanced analytical methods in oncology, offering actionable insights to combat the growing burden of lung cancer.