Implementasi Data Mining Klasterisasi Data Pasien Rawat Inap dengan Algoritma K-Means Clustering - Laksono, Bagas; Syahidin, Yuda; Yunengsih, Yuyun (Jurnal Teknologi Sistem Informasi dan Aplikasi, 2024)
Medical records are data about the history of patients who receive treatment at health care institutions. Along with the development of technology, most healthcare services in Indonesia have now switched from paper-based medical records to digital ones to speed up healthcare services. Despite the good impact of digital medical records, there are various problems, especially for handling huge medical record data. Efficient and effective processing of medical record data is essential to improve the quality of health services. This research utilizes inpatient medical record data into several groups by using the data mining clustering method using the k-means clustering algorithm to handle very large and complicated data by grouping inpatient data into several clusters. Data mining k-means clustering can help organize and analyze medical record data more effectively, so as to improve service quality. The results obtained from this study are inpatient data divided into 4 clusters with 2 variables, namely age category and diagnosis. The results of this analysis obtained data from each cluster, namely cluster 0 disease categories of arthritis, asthma, and cancer patients with adolescent and elderly age categories, cluster 1 disease categories of diabetes, hypertension, and obesity patients with adolescent and elderly age categories, cluster 2 disease categories of diabetes, hypertension, and obesity patients with adult age categories, cluster 3 disease categories of arthritis, asthma, and cancer patients with adult age categories, with a total of 10,000 patients. The findings of this study provide information about the spread of diseases based on the age range of patients, which can be the basis for minimizing the spread of diseases based on age range.