Leveraging a Random Forest Classifier and SVMSMOTE for an Early-stage Dengue Prediction - Bria, Yulianti Paula; Nani, Paskalis Andrianus; Siki, Yovinia Carmeneja Hoar; Mamulak, Natalia Magdalena Rafu; Meolbatak, Emiliana Metan; Guntur, Robertus Dole (Engineering, Technology & Applied Science Research, 2025)
Dengue is a significant global health issue, and in its severe form, it can be fatal. Accurate early-stage prediction tools are crucial in resource-limited areas to prevent dengue’s progression to a severe state. This study aims to develop machine learning classifiers for dengue to assist medical personnel in differentiating the latter from other diseases, thereby helping with its earlier prognosis. Early-stage dengue classifiers were developed using medical records collected from two hospitals in East Nusa Tenggara Province, Indonesia. Eight machine learning techniques were leveraged to develop the classifiers, including Logistic Regression (LR), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), K-nearest Neighbors (KNN), Naïve Bayes (NB), Classification and Regression Tree (CART), Random Forest (RF), and extreme Gradient Boosting (XGBoost). To address the imbalance in the dataset, we utilized the SVM Synthetic Minority Oversampling Technique (SVMSMOTE) was utilized. The dataset was finalized through the expertise of 15 medical doctors and insights gathered from four Indonesian digital health platforms. The key findings of this study are: i) important features for early-stage dengue prediction include fever, duration of fever, headache, arthralgia, myalgia, nausea, shivering, loss of appetite, bitter mouth, temperature, and age, ii) machine learning techniques, including RF, NB, KNN, and XGBoost, were found to be suitable for dengue prediction, and iii) RF combined with SVMSMOTE, outperformed other techniques, achieving an accuracy of 94.99% and an F1-score of 85.65% for early-stage dengue prediction.