Comparative Analysis Of Machine Learning Algorithms For Dengue Fever Prediction Based On Clinical And Laboratory Features

Sriyanto Sriyanto; RZ Abdul Aziz; Dewi Agushinta  Rahayu; Zuriati Zuriati; Mohd Faizal  Abdollah; Irianto Irianto

doi:10.52436/1.jutif.2025.6.6.5309

Authors

Sriyanto Department of Informatics, Institute Informatics and Business Darmajaya, Indonesia
RZ Abdul Aziz Department of Informatics, Institute Informatics and Business Darmajaya, Indonesia
Dewi Agushinta Rahayu Department of Informatics, Institute Informatics and Business Darmajaya, Indonesia
Zuriati Department of Information Technology, Universitas Gunadama, Indonesia
Mohd Faizal Abdollah Department of Internet Engineering Technology, Politeknik Negeri Lampung, Indonesia
Irianto Faculty of Artificial Intelligence and Cyber Security, Universiti Teknikal Malaysia Melaka, Malaysia

DOI:

https://doi.org/10.52436/1.jutif.2025.6.6.5309

Keywords:

Classification Algorithms, Clinical Data, Confusion Matrix, Dengue Fever, Machine Learning, ROC Curve

Abstract

Dengue fever (DF) remains a global health problem requiring accurate early detection to prevent severe complications. This study applies machine learning (ML) algorithms to clinical and laboratory data for improving diagnostic accuracy. Six classifiers were compared: Decision Tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), Naïve Bayes (NB), Neural Network (NN), and Support Vector Machine (SVM). The dataset consists of 1,003 patient records with nine feature columns, of which 989 were used after preprocessing. Class distribution was imbalanced, with 67.6% positive and 32.4% negative cases. Model performance was evaluated using 10-fold cross-validation based on accuracy, precision, recall, F1-score, confusion matrix, and ROC curve analysis. The results indicate that DT achieved the highest performance with 99.4% accuracy, 99.4% precision, 99.7% recall, and 99.6% F1-score, slightly outperforming NN. KNN, LR, and SVM produced comparable results, while NB showed substantially lower accuracy (44.3%) and limited discriminatory power. ROC analysis confirmed these findings, with DT, NN, SVM, and LR achieving AUC values between 0.992 and 0.999, whereas NB performed poorly. These findings highlight the strong potential of ML algorithms, particularly DT, to support medical decision systems, strengthen informatics-based decision support applications, and enhance the accuracy and speed of dengue diagnosis in clinical practice.

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