"An Anonymized Feature-Based Dataset for Lung Cancer Prediction Using Explainable AI Models"

"Early prediction of lung cancer remains a critical challenge in healthcare due to the complexity of risk factors and the need for transparent decision-making systems. This dataset has been developed to address these challenges by providing a feature-based, anonymized dataset for lung cancer prediction using Explainable Artificial Intelligence (XAI) models. The dataset includes structured attributes that represent relevant clinical, demographic, and lifestyle-related factors associated with lung cancer risk, along with corresponding class labels.The dataset is specifically designed to support the development of interpretable machine learning models, enabling researchers to not only achieve accurate predictions but also to understand and explain model decisions. It is suitable for evaluating traditional machine learning algorithms as well as advanced explainable frameworks that emphasize transparency, trust, and generalization. By excluding personally identifiable information and raw medical imaging data, the dataset adheres to ethical and privacy guidelines while remaining highly effective for predictive and explainable AI research. This dataset aims to contribute to reproducible research and foster advancements in explainable healthcare analytics."