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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10856100/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823859658568237056 |
|---|---|
| author | K. M. Tawsik Jawad Anusha Verma Fathi Amsaad Lamia Ashraf |
| author_facet | K. M. Tawsik Jawad Anusha Verma Fathi Amsaad Lamia Ashraf |
| author_sort | K. M. Tawsik Jawad |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-fd8d4ccccba8439690896ec2f34d8c65 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-fd8d4ccccba8439690896ec2f34d8c652025-02-11T00:01:11ZengIEEEIEEE Access2169-35362025-01-0113233122333010.1109/ACCESS.2025.353569210856100A Study on the Application of Explainable AI on Ensemble Models for Predictive Analysis of Chronic Kidney DiseaseK. M. Tawsik Jawad0https://orcid.org/0009-0008-5308-4881Anusha Verma1Fathi Amsaad2https://orcid.org/0000-0002-7582-8326Lamia Ashraf3Department of Computer Science, University of Cincinnati, Cincinnati, OH, USADepartment of Computer Science, Wright State University, Dayton, OH, USADepartment of Computer Science, Wright State University, Dayton, OH, USATangail Medical College and Hospital, Tangail, BangladeshChronic 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.https://ieeexplore.ieee.org/document/10856100/CKDmachine learning ensemble modelsexplainable AIinterpretability |
| spellingShingle | K. M. Tawsik Jawad Anusha Verma Fathi Amsaad Lamia Ashraf A Study on the Application of Explainable AI on Ensemble Models for Predictive Analysis of Chronic Kidney Disease IEEE Access CKD machine learning ensemble models explainable AI interpretability |
| title | A Study on the Application of Explainable AI on Ensemble Models for Predictive Analysis of Chronic Kidney Disease |
| title_full | A Study on the Application of Explainable AI on Ensemble Models for Predictive Analysis of Chronic Kidney Disease |
| title_fullStr | A Study on the Application of Explainable AI on Ensemble Models for Predictive Analysis of Chronic Kidney Disease |
| title_full_unstemmed | A Study on the Application of Explainable AI on Ensemble Models for Predictive Analysis of Chronic Kidney Disease |
| title_short | A Study on the Application of Explainable AI on Ensemble Models for Predictive Analysis of Chronic Kidney Disease |
| title_sort | study on the application of explainable ai on ensemble models for predictive analysis of chronic kidney disease |
| topic | CKD machine learning ensemble models explainable AI interpretability |
| url | https://ieeexplore.ieee.org/document/10856100/ |
| work_keys_str_mv | AT kmtawsikjawad astudyontheapplicationofexplainableaionensemblemodelsforpredictiveanalysisofchronickidneydisease AT anushaverma astudyontheapplicationofexplainableaionensemblemodelsforpredictiveanalysisofchronickidneydisease AT fathiamsaad astudyontheapplicationofexplainableaionensemblemodelsforpredictiveanalysisofchronickidneydisease AT lamiaashraf astudyontheapplicationofexplainableaionensemblemodelsforpredictiveanalysisofchronickidneydisease AT kmtawsikjawad studyontheapplicationofexplainableaionensemblemodelsforpredictiveanalysisofchronickidneydisease AT anushaverma studyontheapplicationofexplainableaionensemblemodelsforpredictiveanalysisofchronickidneydisease AT fathiamsaad studyontheapplicationofexplainableaionensemblemodelsforpredictiveanalysisofchronickidneydisease AT lamiaashraf studyontheapplicationofexplainableaionensemblemodelsforpredictiveanalysisofchronickidneydisease |