Penerapan Decision Tree dan Neural Network untuk Prediksi Severity Level Pada Kasus Hipertensi di RSUD Khidmat Sehat Afiat (KISA) Depok
DOI:
https://doi.org/10.25047/j-remi.v6i1.4963Keywords:
Decision Tree, Classification, Neural Network, Severity LevelAbstract
Severity Level is a component of the INA-CBGs code that indicates the severity of a case during treatment, influencing the INA-CBGs tariff rate. The aim of this study is to predict the severity level by implementing decision tree and neural network algorithms using Orange Data Mining. This research was conducted at Khidmat Sehat Afiat Regional Public Hospital in Depok City, utilizing 162 inpatient claim records with primary diagnoses of Hypertension and Hypertensive Heart Disease and secondary diagnoses of CHF, CKD, or both. Prediction was carried out on 114 testing data and 48 training data. Claim data were analyzed using Decision Tree and Neural Network, with testing results showing the highest score in neural network performance with an AUC of 62.5%, CA of 57%, F1 of 56.2%, and precision of 57.7%. Based on calculations from the confusion matrix, the neural network demonstrated better performance, with accuracy at 57.89%, precision at 65.6%, and recall at 80.76%. These results suggest that the neural network is recommended for predicting the severity level of hypertension cases at Khidmat Sehat Afiat Hospital, as it achieves higher accuracy than the Decision Tree.
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