A Study on the Application of Explainable AI on Ensemble Models for Predictive Analysis of Chronic Kidney Disease
Chronic Kidney Disease (CKD) is one of the widespread Chronic diseases with no known ultimo cure and high morbidity. Research demonstrates that progressive CKD is a heterogeneous disorder that significantly impacts kidney structure and functions, eventually leading to kidney failure. The goal of thi...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10856100/ |
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| Summary: | Chronic Kidney Disease (CKD) is one of the widespread Chronic diseases with no known ultimo cure and high morbidity. Research demonstrates that progressive CKD is a heterogeneous disorder that significantly impacts kidney structure and functions, eventually leading to kidney failure. The goal of this research is to first develop an accurate ensemble model for prediction of unseen cases of CKD given the biomarkers. Also, we have implemented the Explainable AI (XAI) algorithms to interpret the decision-making process of the ensemble models in terms of dominating features and the feature values. The takeaway from our research is to aid the physicians make an informed decision about the disease and provide a case by case explanation behind their decisions. Also, XAI algorithms would allow the patients or subjects understand the causes behind their disease at early stages so that they can be cautious about the progression of the disease to later stages. |
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| ISSN: | 2169-3536 |