DOI : https://doi.org/10.18517/ijaseit.12.6.16226

Comparing Restricted Boltzmann Machine – Backpropagation Neural Networks, Artificial Neural Network – Genetic Algorithm and Artificial Neural Network – Particle Swarm Optimization for Predicting DHF Cases in DKI Jakarta

Bevina D. Handari (1), Dewi Wulandari (2), Nessa A. Aquita (3), Shafira Leandra (4), Devvi Sarwinda (5), Gatot F. Hertono (6)
(1) Department of Mathematics, Universitas Indonesia, Depok, 16424, Indonesia
(2) Department of Mathematics, Universitas Indonesia, Depok, 16424, Indonesia
(3) Department of Mathematics, Universitas Indonesia, Depok, 16424, Indonesia
(4) Department of Mathematics, Universitas Indonesia, Depok, 16424, Indonesia
(5) Department of Mathematics, Universitas Indonesia, Depok, 16424, Indonesia
(6) Department of Mathematics, Universitas Indonesia, Depok, 16424, Indonesia
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B. D. Handari, D. Wulandari, N. A. Aquita, S. Leandra, D. Sarwinda, and G. F. Hertono, “Comparing Restricted Boltzmann Machine – Backpropagation Neural Networks, Artificial Neural Network – Genetic Algorithm and Artificial Neural Network – Particle Swarm Optimization for Predicting DHF Cases in DKI Jakarta”, Int. J. Adv. Sci. Eng. Inf. Technol., vol. 12, no. 6, pp. 2476–2484, Dec. 2022.
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Dengue hemorrhagic fever (DHF) is a common disease in tropical countries such as Indonesia that is often fatal. Early predictions of DHF case numbers help reduce the risk of community transmission and help related authorities develop prevention plans and strategies. Previous research shows that temperature, rainfall, and humidity indirectly affect DHF spread patterns. Therefore, this research uses and compares three machine learning models—restricted Boltzmann machine-backpropagation neural network (RBM-BPNN), artificial neural network-genetic algorithm (ANN-GA), and artificial neural network-particle swarm optimization (ANN-PSO)—to predict DHF case numbers in DKI Jakarta, the capital of Indonesia, which is in the DHF red zone. RBM and PSO are used to calculate optimal initial weight and bias before starting the prediction stage with ANN; meanwhile, GA updates weight and bias during the backward pass in ANN. The data includes temperature, rainfall, and humidity, plus previous DHF case data for five districts in DKI Jakarta from Jan. 6, 2009, to Sept. 25, 2017. We used Arima, Autocorrelation, and Pearson correlation for pre-processing data. The DHF case data fluctuates strongly and requires the moving averages method. The data consists of 70% training data and 30% testing data. The results show that each district requires different model architectures for the best predictions. `The best RMSE prediction of DHF cases with RBM-BPNN in Central Jakarta is 3,78%; the best RMSEs using ANN-GA in North and East Jakarta are 5,65% and 5,99%, respectively. The ANN-PSO model had the largest RMSE value in every district, with an average of 8,43%.

N. Zhao, K. Charland, M. Carabali, E. O. Nsoesie, M. M. Giroux, E. Rees, M. Yuan, C. G. Balaguera, G. Jaramillo Ramirez, and K. Zinszer, “Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia,” PLoS Negl Trop Dis, vol. 14, no. 9, Sep. 2020. DOI: 10.1371/journal.pntd.0008056.

A. W. Maula, A. Fuad, and A. Utarini, “Ten-years trend of dengue research in Indonesia and South-east Asian countries: a bibliometric analysis,” Global Health Action, vol. 11, no. 1, Aug. 2018. DOI: 10.1080/16549716.2018.1504398.

A. A. Suwantika, A. P. Kautsar, W. Supadmi, N. Zakiyah, R, Abdulah, M. Ali, and M. J. Postma, “Cost-Effectiveness of Dengue Vaccination in Indonesia: Considering Integrated Programs with Wolbachia-Infected Mosquitos and Health Education,” International Journal of Environmental Research and Public Health, vol. 17, no. 12, Jun. 2020. DOI: 10.3390/ijerph17124217.

Antaranews, “Health ministry confirms 110.921 dengue fever cases until Oct 2019,” 2019, Accessed on: Mar. 9, 2020, [Online] Available: https://en.antaranews.com/news/135984/healt-ministry-confirms.

iNews ID, “Waspada, Januari hingga Maret 2020 sebanyak 971 kasus DBD di DKI,” 2020, Accessed on: Mar. 15, 2020, [Online] Available: https://www.inews.id/news/megapolitan/waspada-januari-hingga-maret-2020-sebanyak-971-kasus-dbd-di-dki.

T. W. Kesetyaningsih, S. Andarini, Sudarto, and H. Pramoedyo, “Determination of environmental factors affecting dengue incidence in Sleman district, Yogyakarta, Indonesia,” African Journal of Infectious Disease: AJID, vol. 12, sec. 1 Suppl, pp. 13-25, Mar. 2018.

S. N. A. Istiqamah, A. A. Arsin, A. U. Salmah, and A. Mallongi, “Correlation study between elevation, population density, and dengue hemorrhagic fever in Kendari city in 2014-2018,” Open Access Macedonian Journal of Medical Sciences, vol. 8, sec. T2, pp. 63-66, Sep. 2020. DOI: 10.3889/oamjms.2020.5187.

Y. H. Lai “The climatic factors affecting dengue fever outbreaks in Southern Taiwan: An application of symbolic data analysis,” BioMedical Engineering Online, vol. 17, no. 2, pp. 148, Nov. 2018. DOI: 10.1186/s12938-018-0575-4.

P. H. M. N. Herath, A. A. I. Perera, and H. P. Wijekoon, “Prediction of dengue outbreaks in Sri Lanka using artificial neural networks,” International Journal of Computer Applications, vol. 101, no. 15, pp. 0975-8887 Sep. 2014. DOI: 10.5120/17760-8862.

S. F. McGough, L. Clemente, J. N. Kutz, and M. Santillana, “A dynamic, ensemble learning approach to forecast dengue fever epidemic years in Brazil using weather and population susceptibility cycles,” Journal of the Royal Society Interface, vol.18, pp. 179, Jun. 2021. DOI: 10.1098/rsif.2020.1006.

T. Kuremoto, S. Kimura, K. Kobayashi, and M. Obayashi, “Time series forecasting using a deep belief network with restricted Boltzmann machines,” Neurocomputing, vol. 137, pp. 47-56, Aug. 2014.

A Practical Guide to Training Restricted Boltzmann Machines (Version 1), G. Hinton ed., Department of Computer Science, University of Toronto, Toronto, 2010.

J. Wu, Z. Li, L. Zhu, G. Li, B. Niu, and F. Peng, “Optimized BP neural network for dissolved oxygen prediction,” in IFAC, pp. 596-601, Jan. 2018. DOI: 10.1016/j.ifacol.2018.08.132.

T. Chiang, Z. G. Che, and Z. H. Che, “Feed-forward neural networks training: a comparison between genetic algorithm and backpropagation learning algorithm,” International Journal of Innovative Computing, vol. 7, no. 10, pp. 5839-5850, Oct. 2010.

M. A. Sabara, O. Somantri, H. Nurcahyo, N. K. Achmadi, U. Latifah, and Harsono, “Diagnosis classification of dengue fever based on neural networks and genetic algorithms,” Journal of Physics: Conference Series, vol. 1175, no. 1, Mar. 2019. DOI: 10.1088/1742-6596/1175/1/012065.

Epidemiology Surveillance Section of the Health Department of DKI Jakarta. (2019). [Online]. Available: https://surveilansdinkesdki.net/chart.php.

M. Islam, G. Chen, and S. Jin, “An overview of neural network,” American Journal of Neural Networks and Applications, vol. 5, no. 1, Jun. 2019. DOI: 10.11648/j.ajnna.20190501.12.

C. Ou, J. Yang, Z. Du, X. Zhang, and D. Zhu, “Integrating cellular automata with unsupervised deep-learning algorithms: a case study of urban-sprawl simulation in the Jingjintang Urban Agglomeration, China,” Sustainability, vol. 11, no. 9, Jan. 2019. Art. no. 2464.

N. R. Kumar, “Restricted boltzmann machine,” 2018, Accessed on: Mar. 2020, [Online] Available: https://sites.google.com/site/nirajatweb/home/deep_learning_tutorials (2018).

N. G. A. P. H. Saptarini, P. I. Ciptayani, N. W. Wisswani, I. W. Suasnawa and N. E. Indrayana, “Comparing selection method in course scheduling using genetic algorithm,” in International Conference on Science and Technology (ICST 2018), vol. 1, Jan. 2018. DOI:10.2991/icst-18.2018.119.

M. Y. Orong, A. M. Sison, and R. P. Medina, “A new crossover mechanism for genetic algorithm with rank-based selection method,” in 5th International Conference on Business and Industrial Research (ICBIR), May. 2018. DOI:10.1109/ICBIR.2018.8391171.

T. Helmuth, N. F. McPhee, and L. Spector, “Program synthesis using uniform mutation by addition and deletion,” in GECCO ’18, Kyoto, Japan, Jul. 2018. DOI: 10.1145/3205455.3205603.

N. Gómez, L. F. Mingo, J. Bobadilla, F. Serradilla, and J. A. C. Manzano, “Particle swarm optimization models applied to neural networks using the R language,” WSEAS Transactions on Systems, vol. 2, no. 9, pp. 192-202, Feb. 2010.

Y. Chen, J. H. Y. Ong, J. Rajarethinam, G. Yap, L. C. Ng, and A. R. Cook, “Neighbourhood level real-time forecasting of dengue cases in tropical urban Singapore,” BMC medicine, vol. 16, no. 1, Aug. 2018.

V. J Jayaraj, R. Avoi, N. Gopalakrishnan, D. B. Raja, and Y. Umasa, “Developing a dengue prediction model based on climate in Tawau, Malaysia,” Acta tropica, vol. 197, Sep. 2019.

J. M, Jo, “Effectiveness of normalization pre-processing of big data to the machine learning performance,” The Journal of the Korea institute of electronic communication sciences, vol. 14, no. 3, pp. 547-552, Jun. 2019. DOI: 10.13067/JKIECS.2019.14.3.547.

A. Raudys, and Ž. PabarÅ¡kaitÄ—, “Optimising the smoothness and accuracy of moving average for stock price data,” Technological and economic development of economy, vol. 24, no. 3. pp. 984-1003, May. 2018.

Y. Bengio, I. Goodfellow, and A. Courville, “Deep learning,” in MIT Press, Cambridge, Nov. 2015.

T. Chakraborty, S. Chattopadhyay, and I. Ghosh, “Forecasting dengue epidemics using a hybrid methodology,” Physica A: Statistical Mechanics and its Applications, vol. 527, Aug. 2019. DOI: 10.1016/j.physa.2019.121266.

A. Gholamy, V. Kreinovich, and O. Kosheleva, “Why 70/30 or 80/20 relation between training and testing sets: a pedagogical explanation,” Departmental Technical Reports (CS), 2018, [Online] Available: https://scholarworks.utep.edu/cs_techrep/1209/.

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