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Updated: Feb 1, 2026
Assessment of Vascular Function in Patients With Chronic Kidney Disease
Published on: June 16, 2014
Chronic kidney disease detection using XceptionNet with Harmonic Addax Optimization
Suruchi Gaurav Dedgaonkar1, Geeta S Navale1, Priya Shelke1
1Vishwakarma Institute of Technology, Pune, Maharashtra, 411037, India.
Insights
This study introduces an optimized deep learning framework for early Chronic Kidney Disease (CKD) detection. The new method significantly improves diagnostic accuracy, aiding in preventing kidney failure.
Area of Science:
- Nephrology and Artificial Intelligence
- Medical Imaging and Diagnostics
- Computational Health Sciences
Background:
- Chronic Kidney Disease (CKD) is a progressive kidney function impairment leading to serious health complications.
- Early CKD detection is crucial for preventing kidney failure and managing secondary conditions like hypertension and diabetes.
- Existing detection methods face challenges with generalization and class imbalance, hindering accurate early diagnosis.
Purpose of the Study:
- To develop and validate an optimized deep learning framework for enhanced early detection of Chronic Kidney Disease (CKD).
- To address limitations in current CKD detection methods, specifically generalization and class imbalance issues.
- To improve the accuracy and reliability of CKD diagnosis for better patient outcomes.
Main Methods:
- A novel CKD detection framework integrating XceptionNet with the Harmonic Addax Optimization Algorithm (HAOA) was proposed.
- The framework employed sigmoid normalization, feature fusion using a Deep Belief Network (DBN) with a Soergel metric, and Synthetic Minority Overlapping Technique (SMOTE) for data augmentation.
- The HAOA algorithm was developed by combining Harmonic analysis and the Addax Optimization Algorithm (AOA).
Main Results:
- The proposed Xception with HAOA model demonstrated high performance across multiple CKD datasets.
- Achieved a True Positive Rate (TPR) of 94.679%, True Negative Rate (TNR) of 92.777%, and an overall accuracy of 93.667%.
- Reported a precision of 92.258% and an F1-score of 93.453%, indicating robust diagnostic capabilities.
Conclusions:
- The developed deep learning framework offers an effective solution for early CKD diagnosis.
- The integration of XceptionNet and HAOA significantly improves detection accuracy and overcomes existing limitations.
- This approach has the potential to reduce kidney failure rates and enhance patient prognosis through timely intervention.
Abstract:
Chronic Kidney Disease (CKD) refers to a persistent and progressive impairment of kidney function occurring over a prolonged duration. Impaired kidney filtration can lead to the accumulation of waste products and excess fluid in the bloodstream, contributing to the development of secondary medical conditions. CKD leads to high blood pressure, glomerulonephritis, diabetes, and polycystic kidney disease. However, early detection of CKD is significant for decreasing complications and preventing kidney failure. However, generalization and class imbalance issues complicate the detection process. In order to improve CKD detection and resolve current limitations, an optimized deep learning approach is presented in this paper. This paper proposes a CKD detection framework that integrates XceptionNet with the Harmonic Addax Optimization Algorithm (HAOA). First, the chronic kidney dataset is provided as input and undergoes sigmoid normalization to ensure proper data scaling and structural consistency. Next, feature fusion is performed by a Deep Belief Network (DBN) with a Soergel metric. Then, data augmentation is performed utilizing the Synthetic Minority Overlapping Technique (SMOTE). At last, CKD detection is done using Xception with HAOA. Here, HAOA is developed by combining Harmonic analysis and the Addax Optimization Algorithm (AOA). The performance of the proposed Xception with the HAOA method is analyzed by the CKD dataset 1, CKD dataset 2, and the Risk Factor Prediction of CKD Dataset. It also achieves a good True Positive Rate (TPR) value of 94.679 %, True Negative Rate (TNR) of 92.777 %, and accuracy of 93.667 %, a precision of 92.258 %, and an F1-score of 93.453 %. The proposed model serves as an effective tool for early CKD diagnosis, reducing the risk of kidney failure and improving potential outcomes.
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