An Intelligent Diagnostic System to Analyze Early-Stage Chronic Kidney Disease for Clinical Application
Chronic kidney disease (CKD) is a progressive condition characterized by the gradual deterioration of kidney functions, potentially leading to kidney failure if not promptly diagnosed and treated. Machine learning (ML) algorithms have shown significant promise in disease diagnosis, but in healthcare...
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2023/3140270 |
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| author | N. I. Md. Ashafuddula Bayezid Islam Rafiqul Islam |
| author_facet | N. I. Md. Ashafuddula Bayezid Islam Rafiqul Islam |
| author_sort | N. I. Md. Ashafuddula |
| collection | DOAJ |
| description | Chronic kidney disease (CKD) is a progressive condition characterized by the gradual deterioration of kidney functions, potentially leading to kidney failure if not promptly diagnosed and treated. Machine learning (ML) algorithms have shown significant promise in disease diagnosis, but in healthcare, clinical data pose challenges: missing values, noisy inputs, and redundant features, affecting early-stage CKD prediction. Thus, this study presents a novel, fully automated machine learning approach to tackle these complexities by incorporating feature selection (FS) and feature space reduction (FSR) techniques, leading to a substantial enhancement of the model’s performance. A data balancing technique is also employed during preprocessing to address data imbalance issue that is commonly encountered in clinical contexts. Finally, for reliable CKD classification, an ensemble characteristics-based classifier is encouraged. The effectiveness of our approach is rigorously validated and assessed on multiple datasets, and the clinical relevancy of the strategy is evaluated on the real-world therapeutic data collected from Bangladeshi patients. The study establishes the dominance of adaptive boosting, logistic regression, and passive aggressive ML classifiers with 96.48% accuracy in forecasting unseen therapeutic CKD data, particularly in early-stage cases. Furthermore, the effectiveness of the FSR technique in reducing the prediction time significantly is revealed. The outstanding performance of the proposed model demonstrates its effectiveness in addressing the complexity of healthcare CKD data by incorporating the FS and FSR techniques. This highlights its potential as a promising computer-aided diagnosis tool for doctors, enabling early interventions and improving patient outcomes. |
| format | Article |
| id | doaj-art-8c9bc2a709bb45d9b2e8a18ce7340843 |
| institution | Kabale University |
| issn | 1687-9732 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-8c9bc2a709bb45d9b2e8a18ce73408432025-08-20T03:37:46ZengWileyApplied Computational Intelligence and Soft Computing1687-97322023-01-01202310.1155/2023/3140270An Intelligent Diagnostic System to Analyze Early-Stage Chronic Kidney Disease for Clinical ApplicationN. I. Md. Ashafuddula0Bayezid Islam1Rafiqul Islam2Department of Computer Science and EngineeringDepartment of Computer Science & EngineeringDepartment of Computer Science and EngineeringChronic kidney disease (CKD) is a progressive condition characterized by the gradual deterioration of kidney functions, potentially leading to kidney failure if not promptly diagnosed and treated. Machine learning (ML) algorithms have shown significant promise in disease diagnosis, but in healthcare, clinical data pose challenges: missing values, noisy inputs, and redundant features, affecting early-stage CKD prediction. Thus, this study presents a novel, fully automated machine learning approach to tackle these complexities by incorporating feature selection (FS) and feature space reduction (FSR) techniques, leading to a substantial enhancement of the model’s performance. A data balancing technique is also employed during preprocessing to address data imbalance issue that is commonly encountered in clinical contexts. Finally, for reliable CKD classification, an ensemble characteristics-based classifier is encouraged. The effectiveness of our approach is rigorously validated and assessed on multiple datasets, and the clinical relevancy of the strategy is evaluated on the real-world therapeutic data collected from Bangladeshi patients. The study establishes the dominance of adaptive boosting, logistic regression, and passive aggressive ML classifiers with 96.48% accuracy in forecasting unseen therapeutic CKD data, particularly in early-stage cases. Furthermore, the effectiveness of the FSR technique in reducing the prediction time significantly is revealed. The outstanding performance of the proposed model demonstrates its effectiveness in addressing the complexity of healthcare CKD data by incorporating the FS and FSR techniques. This highlights its potential as a promising computer-aided diagnosis tool for doctors, enabling early interventions and improving patient outcomes.http://dx.doi.org/10.1155/2023/3140270 |
| spellingShingle | N. I. Md. Ashafuddula Bayezid Islam Rafiqul Islam An Intelligent Diagnostic System to Analyze Early-Stage Chronic Kidney Disease for Clinical Application Applied Computational Intelligence and Soft Computing |
| title | An Intelligent Diagnostic System to Analyze Early-Stage Chronic Kidney Disease for Clinical Application |
| title_full | An Intelligent Diagnostic System to Analyze Early-Stage Chronic Kidney Disease for Clinical Application |
| title_fullStr | An Intelligent Diagnostic System to Analyze Early-Stage Chronic Kidney Disease for Clinical Application |
| title_full_unstemmed | An Intelligent Diagnostic System to Analyze Early-Stage Chronic Kidney Disease for Clinical Application |
| title_short | An Intelligent Diagnostic System to Analyze Early-Stage Chronic Kidney Disease for Clinical Application |
| title_sort | intelligent diagnostic system to analyze early stage chronic kidney disease for clinical application |
| url | http://dx.doi.org/10.1155/2023/3140270 |
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