PREDIKSI RISIKO DEMAM BERDARAH MENGGUNAKAN DECISION TREE BERDASARKAN GEJALA KLINIS DAN DATA LABORATORIUM

Abstract

Dengue Hemorrhagic Fever (DHF) is a disease caused by the Dengue virus and has a significant impact on public health, especially in tropical areas. Early diagnosis and prediction of DHF risk are essential to prevent complications and improve medical care. This study aims to develop a DHF risk prediction model using the Decision Tree method based on clinical symptoms and laboratory data. The data used include symptoms such as fever, joint pain, rash, and laboratory results such as platelet count and hematocrit. The Decision Tree model was chosen because of its ability to handle data with various variables and provide easy-to-understand interpretations. The research data were taken from patients diagnosed with DHF in several hospitals during a certain period. The dataset was then analyzed to find relevant patterns that could predict a high risk of DHF. The model training and testing process was carried out using cross-validation techniques to ensure prediction accuracy. The results showed that the Decision Tree model had an accuracy rate of 96.95% and consistent results from cross-validation which produced an average accuracy of 92.8%,, with good sensitivity and specificity in predicting DHF risk based on a combination of clinical symptoms and laboratory data. Factors such as low platelet count and fever symptoms lasting more than three days were found to be significant predictive variables. In conclusion, this Decision Tree model has the potential to be used as a tool in early prediction of DHF risk, which can help medical personnel in clinical decision making and patient management. Further development can be done by adding other variables such as epidemiological data to improve model performance.