Abstract
Lung cancer continues to be one of the most widespread and deadly cancer diagnoses that affects humans worldwide. Early detection of lung cancer leads to decreased mortality rates; however, several challenges hinder the development and deployment of effective predictive models. These challenges consist of mainly the problem of high computational power to evaluate large-scale medical data, privacy and security of medical data, limited data sharing between medical organizations and interpretability to handle the black box problem that AI-based models face. Such limitations have posed severe difficulties to the utilization of conventional approaches in the prediction of lung cancer, thus limiting them most importantly for general use, especially in clinical practice settings in real time. To address these challenges, this research introduced a novel lung cancer prediction model that utilizes an integrated framework combining MapReduce, Private Blockchain, Federated Learning (FL), and Explainable Artificial Intelligence (XAI). It improves lung cancer detection using MapReduce to handle large lung cancer datasets, supporting rapid and scalable learning. Private Blockchain is used for the secure, tamper-proof, and immutable processing of patient information, whereas FL allows healthcare sectors to train models together, without compromising patients' privacy. Moreover, it also employed XAI to improve the model's interpretability so clinicians can understand and rely on AI predictions. Together, these methods improve AI's efficiency and trustworthiness in medical applications. This proposed model provides better and more secure lung cancer predictions, ensuring interpretability and collaboration. With an exceptional accuracy of 98.21% and a miss rate of just 1.79%, it outperforms previously published approaches, establishing a new benchmark for privacy-preserving, explainable, and scalable AI models in healthcare.