K-Means Clustering Algorithm Analysis For Grouping Patient Medical Record Data Based On Disease Type
Keywords:
Data Mining, Medical Records, K-Means Clustering Algorithm, Python-based Jupyter NotebookAbstract
Dr. Tengku Mansyur Regional Hospital, Tanjungbalai City, still has difficulty in grouping the intensity of diseases that often occur in Tanjungbalai City, in the exact grouping based on the results of patient medical data records. Medical records are documents that record information about the patient's condition, medical history, and previous treatment filled in by health workers who provide care and treatment. Medical records function to provide health information for all health workers involved in patient care. The goal is to minimize the increase in the intensity of the disease experienced by patients by providing counseling and solutions to diseases that have high intensity. administration in an effort to improve health services in hospitals and also increase the supply of drugs according to the drugs needed. This study uses the Jupyter Notebook application based on Python and data mining methods, especially the K-Means Clustering algorithm, to analyze patient medical record data based on age, blood pressure and disease diagnosis. With the K-Means Clustering Algorithm, it can also minimize variation in one cluster and maximize variation between clusters. With the research, Cluster C1 is a disease that often occurs with high intensity and has 49 members, Cluster C2 is a disease that occurs with intensity. Cluster 2 has 28 members, while Cluster C3 is a disease that occurs with low intensity and has 23 members.
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