HYBRID K MEANS-MULTIVARIATE ADAPTIVE REGRESSION SPLINES FOR DISTRIBUTION OF DENGUE FEVER RISK MAPPING IN BOJONEGORO DISTRICT
Abstract
Dengue Hemorrhagic Fever (DHF) is a dangerous disease transmitted by Aedes aegypti and Aedes albopictus mosquitoes’ bites. WHO data shows that almost half of the world's humans are exposed to Dengue Hemorrhagic Fever. The number of mortality caused by dengue disease is around 20,000 every year. In East Java, Bojonegoro District has the highest number of dengue hemorrhagic fever cases (416). To reduce this number, the causative factors need to be known. Additionally, it's important to pinpoint the region or cluster where the variables driving the spread are located so that prevention and treatment efforts are effective. Based on the elements contributing to the transmission of Dengue Hemorrhagic Fever, this study seeks to identify and categorize locations at risk for the spread of the illness. This study uses Hybrid K Means-Multivariate Adaptive Regression Splines (MARS) which is a combination of K-Means and MARS methods in the hope of providing better analytical results. This is because the data was divided into simpler parts by considering the Oakley distance. The results obtained from the K Means-MARS hybrid shows the relationship between response variables and predictor variables for each cluster. There are three clusters of risk for the spread of dengue hemorrhagic fever in Bojonegoro district with categories: high risk cluster, medium risk cluster and low risk cluster. The high risk cluster consists of 7 sub-districts (Baureno, Kepohbaru, Balen, Sumberrejo, Kedungadem, Bojonegoro and Dander). The variables affecting the DHF Sufferer in the high risk cluster were population density (X2), Altitude (X3) and Health Worker (X6). Meanwhile, the medium risk cluster consists of 10 sub-districts (Kalitidu, Kanor, Kapas, Ngasem, Ngraho, Padangan, Sugihwaras, Sukosewu, Tambakrejo, and Trucuk). The variables that affect the DHF Sufferer in the medium cluster are Number of Dead (X1), Population Density (X2) and Health Facility (X5). The low risk cluster consisted of 11 sub-districts (Bubulan, Gayam, Gondang, Kasiman, Kedewan, Malo, Margomulyo, Ngambon, Purwosari, Sekar, and Temayang). The variables affecting the DHF Sufferer rate in the low risk cluster were number of dead (X1) and population density (X2).
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References
M. U. G. Kraemer et al., “The global distribution of the arbovirus vectors Aedes aegypti and Ae. albopictus,” Elife, vol. 4, p. e08347, 2015, doi: 10.7554/eLife.08347.
Pusat Data dan Informasi Kementerian Kesehatan RI, “Situasi Demam Berdarah Dengue,” InfoDATIN. 2018. [Online]. Available: https://pusdatin.kemkes.go.id/
I. G. N. M. Jaya and H. Folmer, “Bayesian spatiotemporal mapping of relative dengue disease risk in Bandung, Indonesia,” J. Geogr. Syst., vol. 22, no. 1, pp. 105–142, 2020, doi: 10.1007/s10109-019-00311-4.
L. Xu et al., “Climate variation drives dengue dynamics,” Proc. Natl. Acad. Sci., vol. 114, no. 1, pp. 113–118, 2017, doi: 10.1073/pnas.1618558114.
L.-C. Chien and H.-L. Yu, “Impact of meteorological factors on the spatiotemporal patterns of dengue fever incidence,” Environ. Int., vol. 73, pp. 46–56, 2014, doi: https://doi.org/10.1016/j.envint.2014.06.018.
S. A. Thamrin, Aswi, Ansariadi, A. K. Jaya, and K. Mengersen, “Bayesian spatial survival modelling for dengue fever in Makassar, Indonesia,” Gac. Sanit., vol. 35, pp. S59–S63, 2021, doi: https://doi.org/10.1016/j.gaceta.2020.12.017.
F. Kristiani, Y. Claudia, B. Yong, and A.-M. Hilsdon, “A comparative analysis of frequentist and Bayesian approaches to estimate dengue disease transmission in Bandung-Indonesia,” J. Stat. Manag. Syst., vol. 23, no. 8, pp. 1543–1559, Nov. 2020, doi: 10.1080/09720510.2020.1756049.
W. Zhang and A. T. C. Goh, “Multivariate adaptive regression splines and neural network models for prediction of pile drivability,” Geosci. Front., vol. 7, no. 1, pp. 45–52, 2016.
R. M. Adnan, Z. Liang, S. Heddam, M. Zounemat-Kermani, O. Kisi, and B. Li, “Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs,” J. Hydrol., vol. 586, p. 124371, 2020, doi: https://doi.org/10.1016/j.jhydrol.2019.124371.
O. Kisi, P. Khosravinia, M. R. Nikpour, and H. Sanikhani, “Hydrodynamics of river-channel confluence: toward modeling separation zone using GEP, MARS, M5 Tree and DENFIS techniques,” Stoch. Environ. Res. Risk Assess., vol. 33, no. 4, pp. 1089–1107, 2019, doi: 10.1007/s00477-019-01684-0.
A. Ghasemzadeh and M. M. Ahmed, “Utilizing naturalistic driving data for in-depth analysis of driver lane-keeping behavior in rain: Non-parametric MARS and parametric logistic regression modeling approaches,” Transp. Res. Part C Emerg. Technol., vol. 90, pp. 379–392, 2018, doi: https://doi.org/10.1016/j.trc.2018.03.018.
X. Ju, J. M. Rosenberger, V. C. P. Chen, and F. Liu, “Global optimization on non-convex two-way interaction truncated linear multivariate adaptive regression splines using mixed integer quadratic programming,” Inf. Sci. (Ny)., vol. 597, pp. 38–52, 2022.
R. S. Wardani, Purwanto, Sayono, and A. Paramananda, “Clustering tuberculosis in children using K-Means based on geographic information system,” AIP Conf. Proc., vol. 2114, no. June, 2019, doi: 10.1063/1.5112483.
K. S. Ahmed Bin and S. Kamran Jabbar, “Dengue Fever in Perspective of Clustering Algorithms,” J. Data Mining Genomics Proteomics, vol. 06, no. 03, 2015, doi: 10.4172/2153-0602.1000176.
N. Mathur, V. S. Asirvadam, S. C. Dass, and B. S. Gill, “Visualization of dengue incidences for vulnerability using K-means,” in 2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), 2015, pp. 569–573. doi: 10.1109/ICSIPA.2015.7412255.
S. Li et al., “The spatial-temporal trend analysis of schistosomiasis from 1997 to 2010 in Anhui Province, Eastern China,” Am. J. Trop. Med. Hyg., vol. 98, no. 4, pp. 1145–1151, 2018, doi: 10.4269/ajtmh.17-0475.
M. A. Sembiring, “Penerapan Metode Algoritma K-Means Clustering Untuk Pemetaan Penyebaran Penyakit Demam Berdarah Dengue (Dbd),” J. Sci. Soc. Res., vol. 4, no. 3, p. 336, 2021, doi: 10.54314/jssr.v4i3.712.
E. A. Pratama and C. M. Hellyana, “Penerapan Algoritma K-Means Untuk Clustering Data Tempat Penyebaran Jentik Nyamuk Aedes Aegepty Pada Kelurahan Sumampir, Banyumas,” MEANS (Media Inf. Anal. dan Sist., vol. 6, no. 1, pp. 13–18, 2021, doi: 10.54367/means.v6i1.1169.
A. Fariza, Mu’Arifin, and D. W. Astuti, “Spatial-Temporal Visualization of Dengue Haemorrhagic Fever Vulnerability in Kediri District, Indonesia, Using K-means Algorithm,” no. November 2021, pp. 1–6, 2021, doi: 10.1109/icodse53690.2021.9648487.
P. W. Dhewantara et al., “Spatial and temporal variation of dengue incidence in the island of Bali, Indonesia: An ecological study,” Travel Med. Infect. Dis., vol. 32, p. 101437, 2019, doi: https://doi.org/10.1016/j.tmaid.2019.06.008.
Sanusi and J. Husna, “Utilization of Rapidminer using the K-Means Clustering Algorithm for Classification of Dengue Hemorrhagic Fever (DHF) Spread in Banda Aceh City,” J. Inotera, vol. 5, no. 2 SE-Articles, pp. 146–151, Oct. 2020, doi: 10.31572/inotera.Vol5.Iss2.2020.ID119.
S. P. M. Wijayanti et al., “The Importance of Socio-Economic Versus Environmental Risk Factors for Reported Dengue Cases in Java, Indonesia,” PLoS Negl. Trop. Dis., vol. 10, no. 9, p. e0004964, Sep. 2016, [Online]. Available: https://doi.org/10.1371/journal.pntd.0004964
A. I. Widyatami and D. A. Suryawan, “Pengelompokan Daerah Rawan Demam Berdarah Dengue di Provinsi DKI Jakarta,” Indones. Heal. Inf. Manag. J., vol. 9, no. 1, pp. 73–82, 2021.
D. Abdullah, S. Susilo, A. S. Ahmar, R. Rusli, and R. Hidayat, “The application of K-means clustering for province clustering in Indonesia of the risk of the COVID-19 pandemic based on COVID-19 data,” Qual. Quant., vol. 56, no. 3, pp. 1283–1291, 2022.
S. Rahayu and A. Y. Kartini, “ALGORITMA K-MEANS DAN K-MEDOIDS UNTUK PENGELOMPOKAN KECAMATAN PENERIMA BANTUAN SOSIAL DI KABUPATEN BOJONEGORO,” MEDIA BINA Ilm., vol. 16, no. 5, pp. 6815–6822, 2021.
A. Bigdeli, A. Maghsoudi, and R. Ghezelbash, “Application of self-organizing map (SOM) and K-means clustering algorithms for portraying geochemical anomaly patterns in Moalleman district, NE Iran,” J. Geochemical Explor., vol. 233, p. 106923, 2022.
C. Barile, C. Casavola, G. Pappalettera, and V. P. Kannan, “Laplacian score and K-means data clustering for damage characterization of adhesively bonded CFRP composites by means of acoustic emission technique,” Appl. Acoust., vol. 185, p. 108425, 2022.
G.-W. Weber, İ. Batmaz, G. Köksal, P. Taylan, and F. Yerlikaya-Özkurt, “CMARS: a new contribution to nonparametric regression with multivariate adaptive regression splines supported by continuous optimization,” Inverse Probl. Sci. Eng., vol. 20, no. 3, pp. 371–400, Apr. 2012, doi: 10.1080/17415977.2011.624770.
A. Y. K. Kartini and L. N. Ummah, “Pemodelan Kejadian Balita Stunting di Kabupaten Bojonegoro dengan Metode Geographically Weighted Regression dan Multivariate Adaptive Regression Splines,” J Stat. J. Ilm. Teor. dan Apl. Stat., vol. 15, no. 1, 2022.
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