Machine Learning Approaches in Early Lung Cancer Prediction: A Comprehensive Review

Diagnosing cancer at an early stage helps the doctors for the successful treatment of the disease. Lung Cancer, a leading cause of cancer-related deaths globally, has emphasized the importance of early detection to enhance patient survival outcomes. This paper offers a comprehensive review of various machine learning algorithms employed in the prediction and early detection of lung cancer. Through a diligent survey of recent literature, we evaluated the effectiveness of techniques ranging from ensemble learning methods to regression and classification algorithms. Several studies exhibited promising results, with some algorithms achieving accuracies upwards of 99%. Particularly, SVM, Logistic Regression and ensemble methods consistently demonstrated high prediction accuracies across multiple datasets. However, there remains substantial potential to expand on these findings, especially in the domain of hybrid model development, real time predictions and seamless clinical integrations. This review underscores the transformative role of ML in Lung Cancer diagnostics and charts a course for future exploration in this critical medical domain.