Cost-Efficient Early Diagnostic Tool for Lung Cancer: Explainable AI in Clinical Systems

Abstract

Introduction: Lung cancer has the highest mortality rate among all cancer types globally, largely due to delayed or ineffective diagnosis and treatment. Radiomics is commonly used to diagnose lung cancer, especially in later stages or during routine screenings. However, frequent radiological imaging poses health risks, and while advanced diagnostic alternatives exist, they are often costly and accessible only to a limited, privileged population. Leveraging clinical data using machine learning (ML) and artificial intelligence (AI) enables a safer, more inclusive, and affordable solution. Due to a lack of interpretability, AI-based models for cancer diagnosis are less adopted by clinicians. Methods: This study introduces a safe, inclusive, and cost-effective lung cancer diagnostic method using an explainable AI (XAI) model built on routine clinical data. It employs a stacking ensemble of Artificial Neural Network (ANN) and Deep Neural Network (DNN) to match the diagnostic performance of clean-data DNN models. By incorporating rare medical cases through Adaptive Synthetic Sampling (ADASYN), the model reduces the risk of missing challenging, rare-case diagnoses. Results: The proposed XAI model demonstrates strong performance with an accuracy of 0.8558, AUC of 0.8600, precision of 0.8092, recall of 0.9282, and F1-score of 0.8646, notably improving rare case detection by over 50%. SHapley additive exPlanations(SHAP)-based interpretability highlights Erythrocyte sedimentation rate(ESR), intoxication-related factors, hemoglobin levels, and neutrophil counts as key features. The model also reveals associations, such as a link between heavy tobacco use and elevated ESR. Counterfactual explanations help identify features contributing to misdiagnoses by exposing sources of confusion in the model's decisions. Conclusion: Given the limited dataset size and geographic constraints, this research should be viewed as a prototype and in its current form, the model is best suited as a pre-screening tool to support early detection. With training on larger and more diverse datasets, the model has strong potential to evolve into a robust and scalable diagnostic solution.