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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.12704 |
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| _version_ | 1866908541157113856 |
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| author | Khan, Mohammad Abdul Hafeez Bhattacharyya, Twisha Khan, Omar Khan, Noorah Khan, Alina Aziz Fatima Khan, Mohammed Qutub Hajra, Sujoy Ghosh |
| author_facet | Khan, Mohammad Abdul Hafeez Bhattacharyya, Twisha Khan, Omar Khan, Noorah Khan, Alina Aziz Fatima Khan, Mohammed Qutub Hajra, Sujoy Ghosh |
| contents | Chronic Kidney Disease (CKD) affects millions of people worldwide, yet its early detection remains challenging, especially in outpatient settings where laboratory-based renal biomarkers are often unavailable. In this work, we investigate the predictive potential of routinely collected non-renal clinical variables for CKD classification, including sociodemographic factors, comorbid conditions, and urinalysis findings. We introduce the Nephrology-Oriented Representation leArning (NORA) approach, which combines supervised contrastive learning with a nonlinear Random Forest classifier. NORA first derives discriminative patient representations from tabular EHR data, which are then used for downstream CKD classification. We evaluated NORA on a clinic-based EHR dataset from Riverside Nephrology Physicians. Our results demonstrated that NORA improves class separability and overall classification performance, particularly enhancing the F1-score for early-stage CKD. Additionally, we assessed the generalizability of NORA on the UCI CKD dataset, demonstrating its effectiveness for CKD risk stratification across distinct patient cohorts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12704 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NORA: A Nephrology-Oriented Representation Learning Approach Towards Chronic Kidney Disease Classification Khan, Mohammad Abdul Hafeez Bhattacharyya, Twisha Khan, Omar Khan, Noorah Khan, Alina Aziz Fatima Khan, Mohammed Qutub Hajra, Sujoy Ghosh Machine Learning Chronic Kidney Disease (CKD) affects millions of people worldwide, yet its early detection remains challenging, especially in outpatient settings where laboratory-based renal biomarkers are often unavailable. In this work, we investigate the predictive potential of routinely collected non-renal clinical variables for CKD classification, including sociodemographic factors, comorbid conditions, and urinalysis findings. We introduce the Nephrology-Oriented Representation leArning (NORA) approach, which combines supervised contrastive learning with a nonlinear Random Forest classifier. NORA first derives discriminative patient representations from tabular EHR data, which are then used for downstream CKD classification. We evaluated NORA on a clinic-based EHR dataset from Riverside Nephrology Physicians. Our results demonstrated that NORA improves class separability and overall classification performance, particularly enhancing the F1-score for early-stage CKD. Additionally, we assessed the generalizability of NORA on the UCI CKD dataset, demonstrating its effectiveness for CKD risk stratification across distinct patient cohorts. |
| title | NORA: A Nephrology-Oriented Representation Learning Approach Towards Chronic Kidney Disease Classification |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.12704 |