Classification of Patient Disease Trends Based on ICD-10 Diagnosis at Cut Meutia General Hospital Using the C4.5 Method
DOI:
https://doi.org/10.51179/tika.v7i3.1441Keywords:
Algorithm C4.5, Data Mining, ICD-10, Patient Disease TrendsAbstract
The rapid development of information technology has impacted many people getting more and more data every day, even excessive. so that the use of the data is not optimal. Likewise patient medical record data at Cut Meutia General Hospital in North Aceh by serving patients every day. The large number of patients handled automatically makes this hospital accommodate a lot of patient medical record data with various types of diseases so that a method is needed to pattern the patient's disease data. For this reason, this study aims to find trends in patient disease at North Aceh Cut Meutia General Hospital based on ICD-10 diagnostics using data mining techniques by analyzing the C4.5 method where the C4.5 method creates a classification model from a large data set so as to produce patterns the new data pattern forms a decision tree (Decision Tree) useful for exploring data, finding relationships between a number of input variables with a target variable. The data used in this study were obtained from the North Aceh Cut Meutia General Hospital for 2020-2021 based on 4 variable data, namely age, gender, address and ICD-10 diagnosis. ICD-10 is a diagnostic classification with international standards that is compiled based on a category system and reports in disease units according to criteria agreed upon by international experts. ICD-10 (International Statistical Classification of Diseases and Related Health Problems 10th revision)
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