Application of artificial neural network method for early detection of dengue fever
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Published:
Jun 30, 2024
Keywords:
Artificial Neural Networks, Dengue Fever, Diagnostic Accuracy, Machine Learning Techniques, Medical Intervention
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Abstract
Dengue fever is a tropical disease whose diagnosis is often delayed due to limitations of conventional diagnostic methodologies, which have an impact on the effectiveness of medical interventions. This research is designed to develop an Artificial Neural Network (ANN) model aimed at improving accuracy and speed in dengue diagnosis. Through quantitative methods, clinical data from 50 patients during the period 2020-2021 were analyzed using machine learning techniques to train the ANN model, including the process of data normalization and selection of relevant features. The test results of the model showed excellent diagnostic performance with accuracy reaching 87%, precision 92%, and F1-Score 92%, indicating its effective ability to identify positive and negative cases. The conclusion of this study is that the developed ANN model is able to overcome the limitations of conventional diagnostics and shows significant potential in improving medical responses to dengue outbreaks. Further research is recommended to expand the datasets used in order to improve the validation and generalization of the model in the context of broader clinical applications
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Surorejo, S. ., Ningrum, I. L. ., Ananda, P. S. ., & Gunawan, G. . (2024). Application of artificial neural network method for early detection of dengue fever. Journal of Intelligent Decision Support System (IDSS), 7(2), 121-129. https://doi.org/10.35335/idss.v7i2.240
References
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Alem, A., & Kumar, S. (2022). Deep learning models performance evaluations for remote sensed image classification. IEEE Access, 10, 111784–111793. https://doi.org/10.1109/ACCESS.2022.3215264
Ansari, A., Ahmad, I. S., Bakar, A. A., & Yaakub, M. R. (2020). A hybrid metaheuristic method in training artificial neural network for bankruptcy prediction. IEEE Access, 8, 176640–176650. https://doi.org/10.1109/ACCESS.2020.3026529
Buonora, S. N., Passos, S. R. L., Daumas, R. P., Machado, M. G. L., Berardinelli, G. M., de Oliveira, D. N. R., & de Oliveira, R. de V. C. (2020). Pitfalls in acute febrile illness diagnosis: Interobserver agreement of signs and symptoms during a dengue outbreak. Journal of Clinical Nursing, 29(9–10), 1590–1598. https://doi.org/10.1111/jocn.15229
Chow, J. K., Su, Z., Wu, J., Tan, P. S., Mao, X., & Wang, Y.-H. (2020). Anomaly detection of defects on concrete structures with the convolutional autoencoder. Advanced Engineering Informatics, 45, 101105. https://doi.org/10.1016/j.aei.2020.101105
Chung, S. Y., & Abbott, L. F. (2021). Neural population geometry: An approach for understanding biological and artificial neural networks. Current Opinion in Neurobiology, 70(November), 137–144. https://doi.org/10.1016/j.conb.2021.10.010
Cortés-Beringola, A., Vicent, L., Martín-Asenjo, R., Puerto, E., Domínguez-Pérez, L., Maruri, R., Moreno, G., Vidán, M. T., Arribas, F., & Bueno, H. (2021). Diagnosis, prevention, and management of delirium in the intensive cardiac care unit. American Heart Journal, 232, 164–176. https://doi.org/10.1016/j.ahj.2020.11.011
El Khattabi, M.-Z., El Jai, M., Lahmadi, Y., Oughdir, L., & Rahhali, M. (2024). Understanding the interplay between metrics, normalization forms, and data distribution in K-means clustering: a comparative simulation study. Arabian Journal for Science and Engineering, 49(3), 2987–3007. https://doi.org/10.1007/s13369-023-07741-9
Fauzi, I. S., Nuraini, N., Ayu, R. W. S., & Lestari, B. W. (2022). Temporal trend and spatial clustering of the dengue fever prevalence in West Java, Indonesia. Heliyon, 8(8), e10350. https://doi.org/10.1016/j.heliyon.2022.e10350
Goes, F. R., Meerhoff, L. A., Bueno, M. J. O., Rodrigues, D. M., Moura, F. A., Brink, M. S., Elferink-Gemser, M. T., Knobbe, A. J., Cunha, S. A., & Torres, R. S. (2021). Unlocking the potential of big data to support tactical performance analysis in professional soccer: A systematic review. European Journal of Sport Science, 21(4), 481–496. https://doi.org/10.1080/17461391.2020.1747552
Hanna, T. P., King, W. D., Thibodeau, S., Jalink, M., Paulin, G. A., Harvey-Jones, E., O’Sullivan, D. E., Booth, C. M., Sullivan, R., & Aggarwal, A. (2020). Mortality due to cancer treatment delay: systematic review and meta-analysis. Bmj, 371. https://doi.org/10.1136/bmj.m4087
Hasib, K. M., Azam, S., Karim, A., Al Marouf, A., Shamrat, F. M. J. M., Montaha, S., Yeo, K. C., Jonkman, M., Alhajj, R., & Rokne, J. G. (2023). Mcnn-lstm: Combining cnn and lstm to classify multi-class text in imbalanced news data. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3309697
Kurucan, M., Özbaltan, M., Yetgin, Z., & Alkaya, A. (2024). Applications of artificial neural network based battery management systems: a literature review. Renewable and Sustainable Energy Reviews, 192, 114262. https://doi.org/10.1016/j.rser.2023.114262
Li, Y., Dou, Q., Lu, Y., Xiang, H., Yu, X., & Liu, S. (2020). Effects of ambient temperature and precipitation on the risk of dengue fever: A systematic review and updated meta-analysis. Environmental Research, 191, 110043. https://doi.org/10.1016/j.envres.2020.110043
Masi, F., Stefanou, I., Vannucci, P., & Maffi-Berthier, V. (2021). Thermodynamics-based Artificial Neural Networks for constitutive modeling. Journal of the Mechanics and Physics of Solids, 147. https://doi.org/10.1016/j.jmps.2020.104277
Obloj, T., & Sengul, M. (2020). What do multiple objectives really mean for performance? Empirical evidence from the French manufacturing sector. Strategic Management Journal, 41(13), 2518–2547. https://doi.org/10.1002/smj.3198
Rathore, A. P. S., Farouk, F. S., & John, A. L. S. (2020). Risk factors and biomarkers of severe dengue. Current Opinion in Virology, 43, 1–8. https://doi.org/10.1016/j.coviro.2020.06.008
Shaikh, M. S. G., SureshKumar, B., & Narang, G. (2023). Development of optimized ensemble classifier for dengue fever prediction and recommendation system. Biomedical Signal Processing and Control, 85, 104809. https://doi.org/10.1016/j.bspc.2023.104809
Trenfield, S. J., Awad, A., McCoubrey, L. E., Elbadawi, M., Goyanes, A., Gaisford, S., & Basit, A. W. (2022). Advancing pharmacy and healthcare with virtual digital technologies. Advanced Drug Delivery Reviews, 182, 114098. https://doi.org/10.1016/j.addr.2021.114098
Uwishema, O., Nnagha, E. M., Chalhoub, E., Nchasi, G., Mwazighe, R. M., Akin, B. T., Adanur, I., & Onyeaka, H. (2021). Dengue fever outbreak in Cook Island: A rising concern, efforts, challenges, and future recommendations. Journal of Medical Virology, 93(11), 6073–6076. https://doi.org/10.1002/jmv.27223
Wong, J. M., Adams, L. E., Durbin, A. P., Muñoz-Jordán, J. L., Poehling, K. A., Sánchez-González, L. M., Volkman, H. R., & Paz-Bailey, G. (2022). Dengue: a growing problem with new interventions. Pediatrics, 149(6), e2021055522. https://doi.org/10.1542/peds.2021-055522
Wu, T., Wu, Z., & Li, Y. (2022). Dengue fever and dengue virus in the People’s Republic of China. Reviews in Medical Virology, 32(1), e2245. https://doi.org/10.1002/rmv.2245n
Yang, L., Liu, C., Bi, P., Vardoulakis, S., & Huang, C. (2020). Local actions to health risks of heatwaves and dengue fever under climate change: Strategies and barriers among primary healthcare professionals in southern China. Environmental Research, 187, 109688. https://doi.org/10.1016/j.envres.2020.109688
Zhong, N.-N., Wang, H.-Q., Huang, X.-Y., Li, Z.-Z., Cao, L.-M., Huo, F.-Y., Liu, B., & Bu, L.-L. (2023). Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives. Seminars in Cancer Biology. https://doi.org/10.1016/j.semcancer.2023.07.002
Zhou, X., Lu, P., Zheng, Z., Tolliver, D., & Keramati, A. (2020). Accident prediction accuracy assessment for highway-rail grade crossings using random forest algorithm compared with decision tree. Reliability Engineering & System Safety, 200, 106931. https://doi.org/10.1016/j.ress.2020.106931
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