Explainable AI in Tabular Medical Data: A Path to Trustworthy Healthcare Decisions

Accurate diagnosis of kidney disease is essential, as it remains a major health concern affecting individuals across all age groups. Early detection is critical to ensuring timely and effective treatment. With the advancement of deep learning, powerful models now offer end-to-end learning capabilities, extracting relevant patterns directly from complex medical data. These models significantly improve diagnostic precision and support clinical decision-making. However, as deep learning models like ANN, CNN, and LSTM become more complex, the need for interpretability has become increasingly important. The trade-off between model performance and transparency remains a persistent gap in the healthcare domain. To address this concern, the present article uses three black-box models to predict the existence of kidney stones. The models were trained using a dataset of unbalanced kidney stones that was first preprocessed using established methods to increase accuracy and balance. Then ANN, CNN, and LSTM models were used, yielding outstanding accuracies of 97 %, 96 %, and 98 %, respectively. To enhance the interpretability of these black-box models, three XAI technique were applied individually to each model which are Saliency Maps, Ablation, and Permutation Feature Importance. These methods successfully identified the most influential features contributing to the predictions which are Serum Creatinine (sc) and Hemoglobin (hemo). The model's interpretability and high accuracy lay the groundwork for energy-efficient, trustworthy AI applications in 6G-based medical ecosystems. The results demonstrate that combining deep learning with XAI enables both high predictive performance and meaningful model transparency. This integration addresses the critical need for interpretable AI in healthcare and contributes to the development of reliable clinical decision support systems.