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All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Nery Adelia Br Purba
STMIK Kaputama
Automotive Engineering Computer Science & IT Control & Systems Engineering

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Clustering Outpatient and Inpatient Medical Records Based on Patient Complaints using K-Means Nery Adelia Br Purba; Magdalena Simanjuntak; Marto Sihombing
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 6 No. 1 (2026): October 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v6i1.2818

Abstract

Medical record archives contain patterns that can support hospital service planning, but large datasets are difficult to analyze manually. This study developed a clustering application to group outpatient and inpatient medical records at Delia Hospital based on age, patient complaint, and type of service. A total of 600 medical records from 2025 were selected, cleaned, transformed into numerical codes, and processed in MATLAB using the K-means algorithm with squared Euclidean distance. Three scenarios were evaluated, namely K = 3, K = 4, and K = 5, using cluster variance and the distribution of members as comparison measures. The cluster variance decreased from 4.7356 at K = 3 to 2.8841 at K = 4 and 2.0662 at K = 5. The five-cluster solution produced 129, 95, 122, 127, and 127 members, respectively, with dominant complaints of asthma, vertigo, diabetes, fractures, and gastritis. The results indicate that K = 5 provides the most compact and detailed grouping. The resulting information can support the evaluation of outpatient services, inpatient capacity, and hospital resource planning.
Co-Authors Marto Sihombing Simanjuntak, Magdalena