DHF Incidence Rate Prediction Based on Spatial-Time with Random Forest Extended Features

  • Elqi Ashok Telkom University
  • Sri Suryani Prasetiyowati Telkom University
  • Yuliant Sibaroni Telkom University
Keywords: Incidence Rate, DHF, Prediction, Random Forest, Ordinary Kriging


This study proposes a prediction of the classification of the spread of dengue hemorrhagic fever (DHF) with the expansion of the Random Forest (RF) feature based on spatial time. The RF classification model was developed by extending the features based on the previous 2 to 4 years. The three best RF models were obtained with an accuracy of 97%, 93%, and 93%, respectively. Meanwhile, the best kriging model was obtained with an RMSE value of 0.762 for 2022, 0.996 for 2023, and 0.953 for 2024. This model produced a prediction of the classification of dengue incidence rates (IR) with a distribution of 33% medium class and 67% high class for 2022. 2023, the medium class is predicted to decrease by 6% and cause an increase in the high class to 73%. Meanwhile, in 2024, it is predicted that there will be an increase of 10% for the medium class from 27% to 37% and the distribution of the high class is predicted to be around 63%. The contribution of this research is to provide predictive information on the classification of the spread of DHF in the Bandung area for three years with the expansion of features based on time.



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Direktorat Promosi Kesehatan dan Pemberdayaan Masyarakat Kementrian Kesehatan RI, “Demam Berdarah,” Direktorat Promosi Kesehatan dan Pemberdayaan Masyarakat Kementrian Kesehatan RI, Jakarta, 2016.

C. Li, X. Wang, X. Wu, J. Liu, D. Ji, and J. Du, “Modeling and projection of dengue fever cases in Guangzhou based on variation of weather factors,” Sci. Total Environ., vol. 605–606, pp. 867–873, Dec. 2017, doi: 10.1016/J.SCITOTENV.2017.06.181.

S. Yuliant, P. Sri Suryani, and S. Iqbal Bahari, “Determination of dengue hemorrhagic fever disease factors using neural network and genetic algorithms/Yuliant Sibaroni, Sri Suryani Prasetiyowati and Iqbal Bahari Sudrajat,” Math. Sci. Informatics J., vol. 1, no. 2, pp. 77–86, 2020.

A. Salam, S. S. Prasetiyowati, and Y. Sibaroni, “Prediction Vulnerability Level of Dengue Fever Using KNN and Random Forest,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 4, no. 3, pp. 531–536, Jun. 2020, doi: 10.29207/RESTI.V4I3.1926.

I. Alkhaldy, “Modelling the association of dengue fever cases with temperature and relative humidity in Jeddah, Saudi Arabia—A generalised linear model with break-point analysis,” Acta Trop., vol. 168, pp. 9–15, Apr. 2017, doi: 10.1016/J.ACTATROPICA.2016.12.034.

Dinas Kesehatan Kota Bandung, “Profil Kesehatan Kota Bandung Tahun 2019,” Bandung, 2019.

Dinas Kesehatan Kota Bandung, “Profil Kesehatan Kota Bandung Tahun 2020,” Bandung, 2021.

A. S. Fathima and D. Manimeglai, “Analysis of significant factors for dengue infection prognosis using the random forest classifier,” Int. J. Adv. Comput. Sci. Appl., vol. 6, no. 2, pp. 240–245, 2015.

R. Arafiyah, F. Hermin, I. R. Kartika, A. Alimuddin, and I. Saraswati, “Classification of Dengue Haemorrhagic Fever (DHF) using SVM, naive bayes and random forest,” in IOP Conference Series: Materials Science and Engineering, 2018, vol. 434, no. 1, p. 12070.

S. Khan et al., “Random Forest-Based Evaluation of Raman Spectroscopy for Dengue Fever Analysis,” Appl. Spectrosc., pp. 1–7, 2017, doi: https://doi.org/10.1177/0003702817695571.

P. Silitonga, B. E. Dewi, A. Bustamam, and H. S. Al-Ash, “Evaluation of Dengue Model Performances Developed Using Artificial Neural Network and Random Forest Classifiers,” Procedia Comput. Sci., vol. 179, pp. 135–143, 2021, doi: https://doi.org/10.1016/j.procs.2020.12.018.

M. Shahid Ansari et al., “Identification of predictors and model for predicting prolonged length of stay in dengue patients,” Health Care Manag. Sci., vol. 24, no. 4, pp. 786–798, Dec. 2021, doi: 10.1007/S10729-021-09571-3/TABLES/4.

J. Ong et al., “Mapping dengue risk in Singapore using Random Forest,” PLoS Negl. Trop. Dis., vol. 12, no. 6, p. e0006587, Jun. 2018, doi: 10.1371/JOURNAL.PNTD.0006587.

L. Mao, L. Yin, X. Song, and S. Mei, “Mapping intra-urban transmission risk of dengue fever with big hourly cellphone data,” Acta Trop., vol. 162, pp. 188–195, Oct. 2016, doi: 10.1016/J.ACTATROPICA.2016.06.029.

S. S. Prasetiyowati and Y. Sibaroni, “Prediction of DHF disease spreading patterns using inverse distances weighted (IDW), ordinary and universal kriging,” in Journal of Physics: Conference Series, 2018, vol. 971, no. 1, p. 12010.

T. L. Schmidt et al., “Local introduction and heterogeneous spatial spread of dengue-suppressing Wolbachia through an urban population of Aedes aegypti,” PLOS Biol., vol. 15, no. 5, p. e2001894, May 2017, doi: 10.1371/JOURNAL.PBIO.2001894.

M. C. P. Parra et al., “Using adult Aedes aegypti females to predict areas at risk for dengue transmission: A spatial case-control study,” Acta Trop., vol. 182, pp. 43–53, Jun. 2018, doi: 10.1016/J.ACTATROPICA.2018.02.018.

C. Lorenz et al., “Remote sensing for risk mapping of Aedes aegypti infestations: Is this a practical task?,” Acta Trop., vol. 205, p. 105398, May 2020, doi: 10.1016/J.ACTATROPICA.2020.105398.

L. Sedda et al., “The spatial and temporal scales of local dengue virus transmission in natural settings: A retrospective analysis,” Parasites and Vectors, vol. 11, no. 1, pp. 1–14, Feb. 2018, doi: 10.1186/S13071-018-2662-6/TABLES/2.

P. J. Tsai, T. H. Lin, H. J. Teng, and H. C. Yeh, “Critical low temperature for the survival of Aedes aegypti in Taiwan,” Parasites and Vectors, vol. 11, no. 1, pp. 1–14, Jan. 2018, doi: 10.1186/S13071-017-2606-6/TABLES/2.

Direktorat Jenderal Pengendalian Penyakit dan Penyehatan Lingkungan, Pedoman Pencegahan dan Pengendalian Demam Berdarah Dengue di Indonesia. Jakarta: Kementrian Kesehatan RI, 2017.

Y. Zeng, K. Jiang, and J. Chen, “Automatic seismic salt interpretation with deep convolutional neural networks,” ACM Int. Conf. Proceeding Ser., pp. 16–20, Apr. 2019, doi: 10.1145/3325917.3325926.

E. G. Adagbasa, S. A. Adelabu, and T. W. Okello, “Application of deep learning with stratified K-fold for vegetation species discrimation in a protected mountainous region using Sentinel-2 image,” https://doi.org/10.1080/10106049.2019.1704070, vol. 37, no. 1, pp. 142–162, 2019, doi: 10.1080/10106049.2019.1704070.

C. M. Yeşilkanat, “Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm,” Chaos, Solitons & Fractals, vol. 140, p. 110210, 2020.

G. Biau and E. Scornet, “A random forest guided tour,” Test, vol. 25, no. 2, pp. 197–227, Jun. 2016, doi: 10.1007/S11749-016-0481-7.

R. Irmanita, S. S. Prasetiyowati, and Y. Sibaroni, “Classification of Malaria Complication Using CART (Classification and Regression Tree) and Naïve Bayes,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 1, pp. 10–16, Feb. 2021, doi: 10.29207/RESTI.V5I1.2770.

B. George, “A study of the effect of random projection and other dimensionality reduction techniques on different classification methods,” Baselius Res., p. 201769, 2017.

T. Desyani, A. Saifudin, and Y. Yulianti, “Feature Selection Based on Naive Bayes for Caesarean Section Prediction,” in IOP Conference Series: Materials Science and Engineering, 2020, vol. 879, no. 1, p. 12091.

O. I. Sheluhin and V. P. Ivannikova, “Comparative analysis of informative features quantity and composition selection methods for the computer attacks classification using the unsw-nb15 dataset,” T-Comm-Телекоммуникации и Транспорт, vol. 14, no. 10, 2020.

S. S. Prasetiyowati, M. Imrona, I. Ummah, and Y. Sibaroni, “Prediction of public transportation occupation based on several crowd spots using ordinary Kriging method,” J. Innov. Technol. Educ., vol. 3, no. 1, pp. 93–104, 2016.

S. K. Adhikary, N. Muttil, and A. G. Yilmaz, “Genetic Programming-Based Ordinary Kriging for Spatial Interpolation of Rainfall,” J. Hydrol. Eng., vol. 21, no. 2, p. 04015062, Sep. 2015, doi: 10.1061/(ASCE)HE.1943-5584.0001300.

H. Wackernagel, “Ordinary Kriging,” Multivar. Geostatistics, pp. 79–88, 2003, doi: 10.1007/978-3-662-05294-5_11.

How to Cite
Elqi Ashok, Sri Suryani Prasetiyowati, & Yuliant Sibaroni. (2022). DHF Incidence Rate Prediction Based on Spatial-Time with Random Forest Extended Features. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 6(4), 612 - 623. https://doi.org/10.29207/resti.v6i4.4268
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