ItemConference Paper

Clinical Pattern Mining for Early Detection of Chronic Kidney Disease: A Data-Driven Diagnostic Framework

Title
Clinical Pattern Mining for Early Detection of Chronic Kidney Disease: A Data-Driven Diagnostic Framework
Creator
Kaur, Manpreet; Gandhi, Parul; Upreti, Kamal
Description
Early diagnosis of the Chronic Kidney Disease (CKD) is essential to avoid irreversible damage of the kidneys, but it is clear that the traditional threshold-based techniques of the diagnosis are not always able to detect a subtle pattern of biochemical changes, which indicate the early appearance of the disease. This paper provides an interpretable and data-intensive diagnostic model which incorporates clinical state transformation, frequent and contrast pattern mining, and phenotype-based clustering to reveal hidden signs of CKD progression. Continuous laboratory variables are discretized into clinically meaningful states, enabling transparent rule extraction and comparative analysis between CKD and non-CKD cohorts. The mined contrast patterns reveal distinctive early-stage abnormalities, including mild creatinine elevation, reduced urine specific gravity, albuminuria, and increased urea levels, which consistently differentiate diseased patients from healthy controls. Furthermore, K-means clustering identifies three clinically relevant renal phenotypes corresponding to early, moderate, and advanced biochemical deterioration. Sensitivity and comparative analyses demonstrate the robustness of the extracted patterns across varying support thresholds and against standard machine learning classifiers. The proposed framework offers a clinically interpretable and computationally efficient decision-support tool for early CKD detection and patient stratification using routinely collected clinical data. 2026 IEEE.
Source
Proceedings of the 2026 International Conference on AI-Driven Smart Systems and Ubiquitous Computing, ICAUC 2026;pp.898-903
Date
01-01-2026
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Apriori Algorithm; chronic kidney disease (CKD); Clinical Pattern Mining; Clinical State Transformation; Contrast Pattern Analysis; Phenotype Clustering
Coverage
Kaur M., Manav Rachna International Institute of Research & Studies, School of Computer Applications, Department of Computer Applications, Faridabad, India; Gandhi P., Manav Rachna International Institute of Research & Studies, School of Computer Applications, Department of Computer Applications, Faridabad, India; Upreti K., Christ University, School of Sciences, Department of Computer Science, Delhi NCR, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833155851-2;
Format
online
Language
English
Type
Conference paper

Citation

Kaur, Manpreet; Gandhi, Parul; Upreti, Kamal, “Clinical Pattern Mining for Early Detection of Chronic Kidney Disease: A Data-Driven Diagnostic Framework,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 20, 2026, https://archives.christuniversity.in/items/show/25910.

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