Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kaur, Manpreet; Gandhi, Parul; Upreti, Kamal Title A name given to the resource Clinical Pattern Mining for Early Detection of Chronic Kidney Disease: A Data-Driven Diagnostic Framework Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Proceedings of the 2026 International Conference on AI-Driven Smart Systems and Ubiquitous Computing, ICAUC 2026;pp.898-903 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICAUC68182.2026.11441164" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICAUC68182.2026.11441164</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037457621?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037457621?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kaur M., Manav Rachna International Institute of Research &amp; Studies, School of Computer Applications, Department of Computer Applications, Faridabad, India; Gandhi P., Manav Rachna International Institute of Research &amp; 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 Description An account of the resource 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. Subject The topic of the resource Apriori Algorithm; chronic kidney disease (CKD); Clinical Pattern Mining; Clinical State Transformation; Contrast Pattern Analysis; Phenotype Clustering Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155851-2; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online