Improving the Accuracy of Infectious Disease Forecasting with Ensemble Learning

Aldin Muhdar, Syaharuddin Syaharuddin, Aytekin Isman, Muhammad Roil Bilad, Abdilah Abdilah

Abstract


Abstract: This study aims to explore the potential of ensemble learning techniques in improving the accuracy and effectiveness of infectious disease prediction. The research methodology employed is a Systematic Literature Review, gathering literature from indexing databases such as Scopus, DOAJ, and Google Scholar, covering publications from 2013-2023. Inclusion criteria were set to include studies utilizing ensemble learning techniques for predicting infectious diseases. The findings indicate that the use of ensemble learning techniques holds significant potential in enhancing prediction accuracy and effectiveness. Ensemble models demonstrate the ability to produce more accurate and reliable predictions compared to individual models. By integrating various predictive algorithms, ensemble models conduct comprehensive analyses of clinical, laboratory, and imaging data, contributing to improved diagnostic accuracy, therapeutic efficacy assessment, prognosis evaluation, and outbreak prediction. These findings affirm the potential of ensemble learning techniques in enhancing public health preparedness and reducing adverse clinical outcomes associated with infectious diseases. Future studies are encouraged to focus on optimizing the integration of ensemble learning techniques with other complementary intervention approaches and addressing implementation gaps to further enhance the accuracy and effectiveness of infectious disease prediction.

Keywords


Infectious Disease, Ensemble Learning, Accuracy.

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References


Amzat, J., Aminu, K., Kolo, V. I., Akinyele, A. A., Ogundairo, J. A., & Danjibo, M. C. (2020). Coronavirus outbreak in Nigeria: Burden and socio-medical response during the first 100 days. International Journal of Infectious Diseases. https://doi.org/10.1016/j.ijid.2020.06.067

Arza, P. A., Ilham, D., & Hermaiyan, L. (2020). PENGARUH PEMBERIAN PUTIH TELUR TERHADAP KADAR KOLESTEROL TOTAL PASIEN TB PARU. Jurnal Kesehatan. https://doi.org/10.35730/jk.v11i2.497

Asif, D., Bibi, M., Arif, M. S., & Mukheimer, A. (2023). Enhancing Heart Disease Prediction through Ensemble Learning Techniques with Hyperparameter Optimization. Algorithms. https://doi.org/10.3390/a16060308

Awotunde, J. B., Folorunso, S. O., Ajagbe, S. A., Garg, J., & Ajamu, G. J. (2022). AiIoMT: IoMT-Based System-Enabled Artificial Intelligence for Enhanced Smart Healthcare Systems. In Machine Learning for Critical Internet of Medical Things: Applications and Use Cases. https://doi.org/10.1007/978-3-030-80928-7_10

Batteux, E., Mills, F., Jones, L. F., Symons, C., & Weston, D. (2022). The Effectiveness of Interventions for Increasing COVID-19 Vaccine Uptake: A Systematic Review. In Vaccines. https://doi.org/10.3390/vaccines10030386

Burnett, S. M., Mbonye, M. K., Naikoba, S., Stella, Z. M., Kinoti, S. N., Ronald, A., Rubashembusya, T., Willis, K. S., Colebunders, R., Manabe, Y. C., & Weaver, M. R. (2015). Effect of educational outreach timing and duration on facility performance for infectious disease care in Uganda: A trial with pre-post and cluster randomized controlled components. PLoS ONE. https://doi.org/10.1371/journal.pone.0136966

Chen, T., Wang, Y., Chen, H., Marder, K., & Zeng, D. (2014). Targeted Local Support Vector Machine for Age-Dependent Classification. Journal of the American Statistical Association. https://doi.org/10.1080/01621459.2014.881743

Dhanwanth, B., Vivek, B., Abirami, M., Waseem, S. M., & Manikantaa, C. (2023). Forecasting Chronic Kidney Disease Using Ensemble Machine Learning Technique. International Journal on Recent and Innovation Trends in Computing and Communication. https://doi.org/10.17762/ijritcc.v11i5s.7035

Dixon, S., Keshavamurthy, R., Farber, D. H., Stevens, A., Pazdernik, K. T., & Charles, L. E. (2022). A Comparison of Infectious Disease Forecasting Methods across Locations, Diseases, and Time. Pathogens. https://doi.org/10.3390/pathogens11020185

Huang, M., Li, Z., & Zhu, H. (2022). Recent Advances of Graphene and Related Materials in Artificial Intelligence. Advanced Intelligent Systems. https://doi.org/10.1002/aisy.202200077

Ibrahim, A., Abdelsalam, H., & Taj-Eddin, I. (2023). Software Defects Prediction At Method Level Using Ensemble Learning Techniques. International Journal of Intelligent Computing and Information Sciences. https://doi.org/10.21608/ijicis.2023.189934.1251

Jamshidi, M. B., Roshani, S., Talla, J., Lalbakhsh, A., Peroutka, Z., Roshani, S., Parandin, F., Malek, Z., Daneshfar, F., Niazkar, H. R., Lotfi, S., Sabet, A., Dehghani, M., Hadjilooei, F., Sharifi-Atashgah, M. S., & Lalbakhsh, P. (2022). A Review of the Potential of Artificial Intelligence Approaches to Forecasting COVID-19 Spreading. In AI (Switzerland). https://doi.org/10.3390/ai3020028

King, T. E., Bradford, W. Z., Castro-Bernardini, S., Fagan, E. A., Glaspole, I., Glassberg, M. K., Gorina, E., Hopkins, P. M., Kardatzke, D., Lancaster, L., Lederer, D. J., Nathan, S. D., Pereira, C. A., Sahn, S. A., Sussman, R., Swigris, J. J., & Noble, P. W. (2014). A Phase 3 Trial of Pirfenidone in Patients with Idiopathic Pulmonary Fibrosis. New England Journal of Medicine. https://doi.org/10.1056/nejmoa1402582

Kong, W., Zhu, J., Bi, S., Huang, L., Wu, P., & Zhu, S. J. (2023). Adaptive best subset selection algorithm and genetic algorithm aided ensemble learning method identified a robust severity score of COVID-19 patients. In iMeta. https://doi.org/10.1002/imt2.126

Kriss, J. L., Frew, P. M., Cortes, M., Malik, F. A., Chamberlain, A. T., Seib, K., Flowers, L., Ault, K. A., Howards, P. P., Orenstein, W. A., & Omer, S. B. (2017). Evaluation of two vaccine education interventions to improve pertussis vaccination among pregnant African American women: A randomized controlled trial. Vaccine. https://doi.org/10.1016/j.vaccine.2017.01.037

Lu, M., Hou, Q., Qin, S., Zhou, L., Hua, D., Wang, X., & Cheng, L. (2023). A Stacking Ensemble Model of Various Machine Learning Models for Daily Runoff Forecasting. Water (Switzerland). https://doi.org/10.3390/w15071265

Matloob, F., Ghazal, T. M., Taleb, N., Aftab, S., Ahmad, M., Khan, M. A., Abbas, S., & Soomro, T. R. (2021). Software defect prediction using ensemble learning: A systematic literature review. In IEEE Access. https://doi.org/10.1109/ACCESS.2021.3095559

Mbonye, M. K., Burnett, S. M., Naikoba, S., Ronald, A., Colebunders, R., Van Geertruyden, J. P., & Weaver, M. R. (2016). Effectiveness of educational outreach in infectious diseases management: A cluster randomized trial in Uganda. BMC Public Health. https://doi.org/10.1186/s12889-016-3375-4

Muthulakshmi, P., Parveen, M., & Rajeswari, P. (2023). Prediction of Heart Disease using Ensemble Learning. Indian Journal Of Science And Technology. https://doi.org/10.17485/ijst/v16i20.2279

OLIVEIRA, J. S., MACHADO, K. C., & FREITAS, R. M. (2014). NATURAL PRODUCTS APPLIED A NEGLECTED DISEASES: TECHNOLOGICAL FORECASTING. Revista Gestão, Inovação e Tecnologias. https://doi.org/10.7198/s2237-072220140002001

On, N. I., Boongoen, T., & Kongkotchawan, N. (2014). A new link-based method to ensemble clustering and cancer microarray data analysis. International Journal of Collaborative Intelligence. https://doi.org/10.1504/ijci.2014.064842

Peeri, N. C., Shrestha, N., Siddikur Rahman, M., Zaki, R., Tan, Z., Bibi, S., Baghbanzadeh, M., Aghamohammadi, N., Zhang, W., & Haque, U. (2021). The SARS, MERS and novel coronavirus (COVID-19) epidemics, the newest and biggest global health threats: what lessons have we learned? In International Journal of Epidemiology. https://doi.org/10.1093/IJE/DYAA033

Ren, Y., Zhang, L., & Suganthan, P. N. (2016). Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article]. In IEEE Computational Intelligence Magazine. https://doi.org/10.1109/MCI.2015.2471235

Sarker, S., Jamal, L., Ahmed, S. F., & Irtisam, N. (2021). Robotics and artificial intelligence in healthcare during COVID-19 pandemic: A systematic review. In Robotics and Autonomous Systems. https://doi.org/10.1016/j.robot.2021.103902

Sharifi, A., & Khavarian-Garmsir, A. R. (2020). The COVID-19 pandemic: Impacts on cities and major lessons for urban planning, design, and management. In Science of the Total Environment. https://doi.org/10.1016/j.scitotenv.2020.142391

Singh, B., Vijayvargiya, A., & Kumar, R. (2022). Kinematic Modeling for Biped Robot Gait Trajectory Using Machine Learning Techniques. Journal of Bionic Engineering. https://doi.org/10.1007/s42235-021-00142-4

Solonen, A., Bibov, A., Bardsley, J. M., & Haario, H. (2014). Optimization-based sampling in ensemble Kalman filtering. International Journal for Uncertainty Quantification. https://doi.org/10.1615/Int.J.UncertaintyQuantification.2014007658

Vammi, V., Lin, T. L., & Song, G. (2014). Enhancing the quality of protein conformation ensembles with relative populations. Journal of Biomolecular NMR. https://doi.org/10.1007/s10858-014-9818-2

Wu, H., & Levinson, D. (2021). The ensemble approach to forecasting: A review and synthesis. Transportation Research Part C: Emerging Technologies. https://doi.org/10.1016/j.trc.2021.103357

Ye, J. (2020). The role of health technology and informatics in a global public health emergency: Practices and implications from the COVID-19 pandemic. In JMIR Medical Informatics. https://doi.org/10.2196/19866

Zheng, R., & Liu, G. (2023). Application of machine learning in clinical predictive models for infectious diseases: a review. Chinese Journal of Schistosomiasis Control. https://doi.org/10.16250/j.32.1374.2023084

Zhu, J., Zhang, A., & Zheng, H. (2023). Research on Predictive Model Based on Ensemble Learning. Highlights in Science, Engineering and Technology. https://doi.org/10.54097/hset.v57i.10023


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