Explainable AI for Disease Diagnosis Feature-Based and Visual Interpretability in CKD and Lung Disease Detection

• Author(s): Prof. Leena Shah; Raj Tiwari; Sumit Singh; Sahil Jadhav; Ajay Kumawat
• Paper ID: 1715093
• Page: 865-871
• Published Date: 13-03-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 9 March-2026

Abstract

Explainable Artificial Intelligence (XAI) is crucial in medical diagnosis to enhance transparency, interpretability, and trust in AI-driven decision-making. This study explores the application of XAI techniques for Chronic Kidney Disease (CKD) prediction and lung disease detection. For CKD, a machine learning model was developed using patient clinical data, with SHAP (SHapley Additive exPlanations) employed to identify the most influential features affecting predictions. The results demonstrate that attributes such as serum creatinine, blood urea, and hemoglobin levels significantly impact CKD risk assessment. For lung disease detection, a deep learning model was trained on chest X-ray images, and Grad-CAM (Gradient-weighted Class Activation Mapping) was applied to generate visual explanations, highlighting the critical regions influencing model decisions. Experimental results show that both methods improve the interpretability of AI predictions, aiding healthcare professionals in understanding and validating the model’s reasoning. The study highlights the importance of integrating explainability into medical AI models to ensure reliability, facilitate clinical adoption, and enhance patient trust. Future work includes expanding the dataset and exploring additional XAI techniques to further improve model transparency.

Keywords

Explainable AI (XAI), Chronic Kidney Disease (CKD) Prediction, Lung Disease Detection, SHAP (SHapley Additive exPlanations), Grad-CAM (Gradient-weighted Class Activation Mapping), Medical AI Interpretability

Citations

IRE Journals:
Prof. Leena Shah, Raj Tiwari, Sumit Singh, Sahil Jadhav, Ajay Kumawat "Explainable AI for Disease Diagnosis Feature-Based and Visual Interpretability in CKD and Lung Disease Detection" Iconic Research And Engineering Journals Volume 9 Issue 9 2026 Page 865-871 https://doi.org/10.64388/IREV9I9-1715093

IEEE:
Prof. Leena Shah, Raj Tiwari, Sumit Singh, Sahil Jadhav, Ajay Kumawat "Explainable AI for Disease Diagnosis Feature-Based and Visual Interpretability in CKD and Lung Disease Detection" Iconic Research And Engineering Journals, 9(9) https://doi.org/10.64388/IREV9I9-1715093