A Review on Multi-Model Machine Learning Architectures for Early Prediction of Lung Cancer Using Scikit-Learn

Early detection of lung cancer is critical for improving patient survival rates, yet current diagnostic practices often fail to identify the disease at its initial stages. Recent advancements in machine learning (ML) have enabled the development of automated, efficient, and accurate diagnostic systems. This review paper presents a comprehensive analysis of multi-model machine learning architectures applied to the early prediction of lung cancer, with a particular focus on frameworks developed using Scikit-Learn. The study examines various ML models—including ensemble methods, hybrid pipelines, feature selection techniques, and model stacking—that enhance predictive accuracy and robustness. Key datasets, preprocessing approaches, hyperparameter optimization strategies, and evaluation metrics are assessed to highlight their impact on diagnostic performance. Furthermore, the review compares the strengths and limitations of individual and combined ML models for lung cancer prediction. The findings emphasize that multi-model architectures implemented with Scikit-Learn significantly outperform single-model approaches by effectively leveraging complementary learning patterns. Finally, the paper outlines existing challenges, emerging trends, and future research opportunities for developing more reliable and clinically deployable ML-driven lung cancer early detection systems.