A Comparative Study of Machine Learning Based Classifications on Lung Cancer Detection

This research addresses a significant gap in lung cancer prediction, focusing on the critical need for highly accurate models to improve early detection and treatment outcomes. Despite advances in machine learning, achieving higher accuracy in classification models for lung cancer remains a persistent challenge. This study aims to fill this gap by developing advanced machine-learning models specifically designed to enhance the prediction of lung cancer outcomes. The research employs a rigorous methodology that involves the systematic exploration of various machine learning algorithms applied to a comprehensive lung cancer dataset. Through meticulous phases of model development, training, validation, and testing, this study evaluates the performance and accuracy of the proposed models. The findings underscore the effectiveness of these models in addressing the challenge of early lung cancer detection, offering more precise and reliable classification outcomes than existing approaches. The novelty of this research lies in the extensive experimental work, which demonstrates the superiority of the selected algorithm for lung cancer data classification. This not only highlights the potential of the algorithm for clinical application but also provides a concrete solution to the ongoing issue of achieving higher accuracy in lung cancer prediction models. By bridging this research gap, the study offers promising new avenues for earlystage lung cancer prognosis and treatment strategies, with the potential to significantly impact clinical practices.