Published Paper Details:

This study presents a comparative analysis of Naive Bayes, Random Forest, and Gradient Boosting algorithms for predicting the survival of lung cancer patients. As lung cancer continues to be one of the leading causes of cancer-related deaths globally, accurate prediction is essential for treatment planning and patient care. Here, these machine learning methods are used to create predictive models by utilizing a dataset that included clinical variables and patient outcomes. Each model's performance was evaluated using metrics such as accuracy, precision, recall, and F1-score. Furthermore, a feature importance analysis was carried out to pinpoint the critical prognostic parameters affecting the prediction of survival. Our results demonstrate the effectiveness of Gradient Boosting in achieving the highest predictive performance, followed by Random Forest and Naive Bayes. Furthermore, the feature importance analysis revealed critical clinical variables contributing to survival prognosis, providing insights into the underlying factors influencing lung cancer patient outcomes. This study plays a pivotal role in advancing personalized medicine by enabling more precise survival prognoses for individuals diagnosed with lung cancer. Such insights empower clinicians to make well- informed decisions regarding treatment strategies, ultimately enhancing the quality of patient care.