OPTIMIZATION OF MACHINE LEARNING ALGORITHMS IN THE CLASSIFICATION OF VECTOR-BORNE DISEASES
DOI:
https://doi.org/10.33480/techno.v20i2.6539Keywords:
Disease Control , Disease Prediction , Machine Learning (ML) , SMOTE , Vector-Borne DiseasesAbstract
Developing a predictive model is the objective of this study, focusing on vector-borne diseases using various machine learning methods, including Random Forest (RF), Logistic Regression (LR), k-nearest Neighbors (kNN), Tree (DT), and XGBoost. The main goal is to use oversampling techniques like SMOTE and Random Oversampling to correct the dataset's class imbalance. The dataset was obtained from Kaggle and literature references published in Frontiers in Ecology and Evolution (Endo and Amarasekare 2022), consisting of approximately 9,490 entries with environmental, demographic, and clinical attributes. Dengue Fever is one of the diseases that this study focuses on. Aedes aegypti mosquitoes spread it, and it is a significant health risk in tropical areas. The DT and XGBoost models had the highest accuracy, at 99.2%. Logistic Regression and Random Forest also did well, with 99.1% accuracy. KNN did well, too, but with a lower recall, at 99.0%. The ROC curve gave a complete picture of how well each model classified things. These findings indicate that when combined with proper data handling, machine learning models can significantly improve early detection of vector-borne diseases and support more accurate and timely decision-making in public health interventions.
References
Babawarun, Oloruntoba, Chioma Anthonia Okolo, Jeremiah Olawumi Arowoogun, Adekunle Oyeyemi Adeniyi, and Rawlings Chidi. 2024. “Healthcare Managerial Challenges in Rural and Underserved Areas: A Review.” World Journal of Biology Pharmacy and Health Sciences 17 (2): 323–30. https://doi.org/10.30574/wjbphs.2024.17.2.0087.
Dirantara, Reza, and Febri Sugandi. 2025. “Prediksi Calon Kelulusan Mahasiswa Menggunakan Algoritma K-Nearest Neighbor (K-NN).” Journal of Science and Social Research 8 (1): 552–56. https://doi.org/10.54314/jssr.v8i1.2748.
Endo, Andrew, and Priyanga Amarasekare. 2022. “Predicting the Spread of Vector-Borne Diseases in a Warming World.” Frontiers in Ecology and Evolution 10 (April). https://doi.org/10.3389/fevo.2022.758277.
Febiriana, Iffatricia Haura, Abdullah Hasan Hassan, and Dipo Aldila. 2024. “Enhancing Malaria Control Strategy: Optimal Control and Cost-Effectiveness Analysis on the Impact of Vector Bias on the Efficacy of Mosquito Repellent and Hospitalization.” Journal of Applied Mathematics 2024 (March).
Kamguem, Inès Sopbué, Nathalie Kirschvink, Abel Wade, and Catherine Linard. 2025. “Determinants of Viral Haemorrhagic Fever Risk in Africa’s Tropical Moist Forests: A Scoping Review of Spatial, Socio-Economic, and Environmental Factors.” Plos Neglected Tropical Diseases 19 (1). https://doi.org/10.1371/journal.pntd.0012817.
Kumar, Athira Satheesh, Chris T. Bauch, and Madhur Anand. 2025. “Climate-Denying Rumor Propagation in a Coupled Socio-Climate Model: Impact on Average Global Temperature.” Plos One 20 (1). https://doi.org/10.1371/journal.pone.0317338.
Lin, Tai Han, Hsing Yi Chung, Ming Jr Jian, Chih Kai Chang, Hung Hsin Lin, Chiung Tzu Yen, Sheng Hui Tang, et al. 2025. “AI-Driven Innovations for Early Sepsis Detection by Combining Predictive Accuracy with Blood Count Analysis in an Emergency Setting: Retrospective Study.” Journal of Medical Internet Research 27 (1). https://doi.org/10.2196/56155.
Manikandan, S., A. Mathivanan, Bhagyashree Bora, P. Hemaladkshmi, V. Abhisubesh, and S. Poopathi. 2022. “A Review on Vector Borne Diseases and Various Strategies to Control Mosquito Vectors.” Indian Journal of Entomology 86 (1): 329–38. https://doi.org/10.55446/IJE.2023.410.
Piscitelli, Prisco, and Alessandro Miani. 2024. “Climate Change and Infectious Diseases: Navigating the Intersection through Innovation and Interdisciplinary Approaches.” International Journal of Environmental Research and Public Health. https://doi.org/10.3390/ijerph21030314.
Pise, Reshma, and Kailas Patil. 2023. “A Deep Transfer Learning Framework for the Multi-Class Classification of Vector Mosquito Species.” Journal of Ecological Engineering 24 (9): 183–91. https://doi.org/10.12911/22998993/168501.
Priyono, Eko, Teddy Al Fatah, Sukrul Ma’mun, and Faruq Aziz. 2023. “Tubercolusis Segmentation Based on X-Ray Images.” Journal Medical Informatics Technology 1 (4): 101–4. https://doi.org/10.37034/medinftech.v1i4.22.
Priyono, Eko, Ispandi, and Rusdi. 2024. “Evaluating the Impact of Agricultural Technology on Greenhouse Gas Emissions Using Machine Learning.” Journal of Information Systems and Informatics 6 (4). https://doi.org/https://doi.org/10.51519/journalisi.v6i4.870.
Ramadhan, Nur Ghaniaviyanto, and Azka Khoirunnisa. 2021. “Klasifikasi Data Malaria Menggunakan Metode Support Vector Machine.” Jurnal Media Informatika Budidarma 5 (4): 1580–84. https://doi.org/10.30865/mib.v5i4.3347.
Saputra, Rendy Amy, and Aditya Pratama. 2025. “Implementasi Decision Tree Untuk Prediksi Harga Rumah Di Daerah Tebet.” Journal of Information System Management (JOISM) e-ISSN 6 (2): 2715–3088. https://doi.org/10.24076/joism.2025v6i2.1928.
Shaikh, Salim Gulab, Billakurthi Suresh Kumar, Geetika Narang, and Nishant Nilkanth Pachpor. 2024. “Hybrid Machine Learning Method for Classification and Recommendation of Vector-Borne Disease.” Journal of Autonomous Intelligence 7 (2). https://doi.org/10.32629/jai.v7i2.797.
Sobari, Syahrul, Ade Irma Purnamasari, Agus Bahtiar, and Kaslani. 2025. “Meningkatkan Model Prediksi Kelulusan Santri Tahfidz Di Pondok Pesantren Al-Kautsar Menggunakan Algoritma Random Forest.” Jurnal Informatika Dan Teknik Elektro Terapan 13 (1). https://doi.org/10.23960/jitet.v13i1.5704.
Zhang, Dongsong, and Tianhua Chen. 2024. “Scikit-ANFIS: A Scikit-Learn Compatible Python Implementation for Adaptive Neuro-Fuzzy Inference System.” International Journal of Fuzzy Systems 26 (6): 2039–57. https://doi.org/10.1007/s40815-024-01697-0.
Zhen, Jianing, Dehua Mao, Zhen Shen, Demei Zhao, Yi Xu, Junjie Wang, Mingming Jia, Zongming Wang, and Chunying Ren. 2024. “Performance of XGBoost Ensemble Learning Algorithm for Mangrove Species Classification with Multisource Spaceborne Remote Sensing Data.” Journal of Remote Sensing 4 (June). https://doi.org/10.34133/remotesensing.0146.
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