Advancing vector-borne disease prediction through functional classifier integration: A novel approach for enhanced modeling: Machine learning tools and vector-borne disease prediction

Mokammel Hossain Tito; Most Hoor E  Jannat; Mst Tachhlima  Aktar; Barshon  Saha; Puja  Das; Md.  Kawser; Md. Arafat  Hossain; Md. Neyamul  Islam; Muhammad Shahzad                                 Chohan; Shahzad  Khan

doi:10.62310/liab.v4i1.135

Authors

Mokammel Hossain Tito Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Bangladesh https://orcid.org/0009-0006-0082-9953
Most Hoor E Jannat Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Bangladesh https://orcid.org/0009-0005-0069-2283
Mst Tachhlima Aktar Bangladesh Agricultural University, Mymensingh, Bangladesh https://orcid.org/0000-0003-3339-7094
Barshon Saha Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Bangladesh https://orcid.org/0009-0004-1219-716X
Puja Das Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Bangladesh
Md. Kawser Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Bangladesh https://orcid.org/0009-0009-0925-924X
Md. Arafat Hossain Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Bangladesh
Md. Neyamul Islam Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Bangladesh https://orcid.org/0009-0008-9542-4792
Muhammad Shahzad Chohan Department of Biomedical Sciences, King Faisal University, Al Hafuf, Saudi Arabia https://orcid.org/0000-0001-6539-7334
Shahzad Khan Department of Biomedical Sciences, King Faisal University, Al Hafuf, Saudi Arabia

DOI:

https://doi.org/10.62310/liab.v4i1.135

Keywords:

Machine learning models, Vector borne disease, Accuracy, Efficacy, Simple logistic

Abstract

This paper evaluates various machine learning models for predicting vector-borne diseases, focusing on performance metrics that reveal insights into their efficacy. The Multilayer Perceptron (MLP) model demonstrated the highest accuracy at 92%, surpassing the Simple Logistic (SL) and Support Vector Machine (SVM) models, which achieved 88% and 90.87% accuracy, respectively. Notably, the MLP model excelled in precision, recall, and F-Measure, indicating superior classification accuracy. Conversely, the SVM model exhibited noteworthy computational efficiency with the lowest processing time at 0.3 seconds, emphasizing its potential for real-time applications in public health interventions. In contrast, the Radial Basis Function Network (RBFN) lagged in accuracy and other metrics. The results underscore the trade-offs between accuracy and computational efficiency, emphasizing the need for a nuanced model selection. Considering the holistic evaluation, the SVM model emerged as a compelling choice, balancing high accuracy and efficient processing, making it promising for real-time public health applications. This study contributes valuable insights into machine learning model performance, emphasizing the importance of selecting models tailored to the specific needs of vector-borne disease prediction. As we confront emerging infectious diseases, the SVM model stands as an indispensable tool, supporting a proactive and data-driven approach to mitigate the global health impact of vector- borne diseases.

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References

Alfred R, Obit JH. (2021). The roles of machine learning methods in limiting the spread of deadly diseases: A systematic review. Heliyon 7(6): e07371. https://doi.org/10.1016/j.heliyon.2021.e07371

Almustafa KM. (2020). Prediction of heart disease and classifiers’ sensitivity analysis. BMC Bioinformatics 21(1): 278. https://doi.org/10.1186/s12859-020-03626-y

Arifuzzaman M, Islam M, Hossain M, Tito MH, Anwar M, Fuhaid AA. (2021). Application of AI on moisture damage of modified asphalt binders. 4th Smart Cities Symposium (SCS), Online Conference, Bahrain, 21-23 November 2021, pp. 307–311. https://doi.org/10.1049/icp.2022.0361

Basu S, Johnson KT, Berkowitz SA. (2020). Use of Machine Learning Approaches in Clinical Epidemiological Research of Diabetes. Current Diabetes Reports 20(12): 80. https://doi.org/10.1007/s11892-020-01353-5

Brownstein JS, Rosen H, Purdy D, Miller JR, Merlino M, Mostashari F, Fish D. (2002). Spatial Analysis of West Nile Virus: Rapid Risk Assessment of an Introduced Vector-Borne Zoonosis. Vector-Borne and Zoonotic Diseases 2(3): 157–164. https://doi.org/10.1089/15303660260613729

da Silva CC, de Lima CL, da Silva ACG, Moreno GMM, Musah A, Aldosery A, Dutra L, Ambrizzi T, Borges IVG, Tunali M, Basibuyuk S, Yenigun O, Massoni TL, Jones K, Campos L, Kostkova P, da Silva Filho AG, dos Santos WP. (2022). Spatiotemporal forecasting for dengue, chikungunya fever and Zika using machine learning and artificial expert committees based on meta-heuristics. Research on Biomedical Engineering 38(2): 499–537. https://doi.org/10.1007/s42600-022-00202-6

Eisen L, Eisen RJ. (2011). Using Geographic Information Systems and Decision Support Systems for the Prediction, Prevention, and Control of Vector-Borne Diseases. Annual Review of Entomology 56(1): 41–61. https://doi.org/10.1146/annurev-ento-120709-144847

Erraguntla M, Zapletal J, Lawley M. (2019). Framework for Infectious Disease Analysis: A comprehensive and integrative multi-modeling approach to disease prediction and management. Health Informatics Journal 25(4): 1170 – 1187. https://doi.org/10.1177/1460458217747112

Fuchida M, Pathmakumar T, Mohan RE, Tan N, Nakamura A. (2017). Vision-Based Perception and Classification of Mosquitoes Using Support Vector Machine. Applied Sciences 7(1): 51. https://doi.org/10.3390/app7010051

Gubler DJ. (2009). Vector-borne diseases. Revue Scientifique et Technique (International Office of Epizootics) 28(2): 583 – 588. https://doi.org/10.20506/rst.28.2.1904

Javaid M, Sarfraz MS, Aftab MU, Zaman Q. uz, Rauf HT, Alnowibet KA. (2023). Web GIS-Based Real-Time Surveillance and Response System for Vector-Borne Infectious Diseases. International Journal of Environmental Research and Public Health 20(4): 4. https://doi.org/10.3390/ijerph20043740

Kaur I, Sandhu AK, Kumar Y. (2022). Artificial Intelligence Techniques for Predictive Modeling of Vector-Borne Diseases and its Pathogens: A Systematic Review. Archives of Computational Methods in Engineering, 29(6), 3741–3771. https://doi.org/10.1007/s11831-022-09724-9

Kesorn K, Ongruk P, Chompoosri J, Phumee A, Thavara U, Tawatsin A, Siriyasatien P. (2015). Morbidity Rate Prediction of Dengue Hemorrhagic Fever (DHF) Using the Support Vector Machine and the Aedes aegypti Infection Rate in Similar Climates and Geographical Areas. PLOS ONE 10(5): e0125049. https://doi.org/10.1371/journal.pone.0125049

Knudsen AB, Slooff R. (1992). Vector-borne disease problems in rapid urbanization: New approaches to vector control. Bulletin of the World Health Organization 70(1): 1–6.

Kofidou M, de Courcy Williams M, Nearchou A, Veletza S, Gemitzi A, Karakasiliotis I. (2021). Applying Remotely Sensed Environmental Information to Model Mosquito Populations. Sustainability 13(14): 7655. https://doi.org/10.3390/su13147655

Kumar S, Srivastava A, Maity R. (2024). Modeling climate change impacts on vector-borne disease using machine learning models: Case study of Visceral leishmaniasis (Kala-azar) from Indian state of Bihar. Expert Systems with Applications 237: 121490. https://doi.org/10.1016/j.eswa.2023.121490

Raizada S, Mala S, Shankar A. (2020). Vector Borne Disease Outbreak Prediction by Machine Learning. 2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), Bengaluru, India, pp. 213–218. https://doi.org/10.1109/ICSTCEE49637.2020.9277286

Raizada S, Mala S, Shankar A. (2021). Vector-Borne Disease Outbreak Prediction Using Machine Learning Techniques. In: Prakash KB, Kannan R, Alexander SA, Kanagachidambaresan GR, editors, Advanced Deep Learning for Engineers and Scientists. EAI/Springer innovations in Communication and Computing. Springer Cham. Pp. 227–241. https://doi.org/10.1007/978-3-030-66519-7_9

Shaikh SG, Kumar BS, Narang G, Pachpor NN. (2023). Diagnosis of Vector Borne Disease using Various Machine Learning Techniques. International Journal of Intelligent Systems and Applications in Engineering 11(4s): 517-526. https://ijisae.org/index.php/IJISAE/article/view/2721

Tito MH, Arifuzzaman M, Jannat MHE, Rahman MS, Sharmy ST, Nasrin A, Asaduzzaman M, Ashrafuzzaman M, Prince DB, Asif AH. (2023). A comparative study of ensemble machine learning algorithms for brucellosis disease prediction. Letters In Animal Biology 3(2): 23-27. https://doi.org/10.62310/liab.v3i2.119

Uddin S, Khan A, Hossain ME, Moni MA. (2019). Comparing different supervised machine learning algorithms for disease prediction. BMC Medical Informatics and Decision Making 19(1): 281. https://doi.org/10.1186/s12911-019-1004-8

WHO (2014). World Health Organization, Regional Office for South-East Asia. Vector-borne diseases. https://iris.who.int/handle/10665/206531

Wilson AL, Courtenay O, Kelly-Hope LA, Scott TW, Takken W, Torr SJ, Lindsay SW. (2020). The importance of vector control for the control and elimination of vector-borne diseases. PLOS Neglected Tropical Diseases 14(1): e0007831. https://doi.org/10.1371/journal.pntd.0007831