Determination of dengue hemorrhagic fever disease factors using neural network and genetic algorithms
Abstract
The Aedes aegypti mosquito and the Aedes albopictus mosquito are carriers of the virus that causes Dengue Hemorrhagic Fever (DHF). In Indonesia, the spread of DHF disease has taken place for 41 years. Within this period, there was an increase in the number of spreading areas by 97% and an increase in the number of cases by 99%. Based on the data from previous studies, further information is needed related to the factors that have the most influence on the level of DHF infection in a region. Based on the initial study conducted, there are 6 factors that have the potential to influence the number of DHF cases in an area, namely temperature (X1), rainfall (X2), population density (X3), altitude (X4), distribution of males (X5), and distribution of education level (X6). In this study, the problem of determination dengue disease factors was modeled using a neural network. The activation function in this neural network model then estimated using genetic algorithms. Determination of the best factor is carried out in a genetic algorithm by combining several parameters of the crossover probability (Pc) and mutation probability (Pm). This experiment show that the main factors that influence the spread of DHF in Bandung area are temperature, altitude, distribution of gender, and distribution of education levels. The best accuracy system obtained in this study using these 4 factors reached 72%.
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[3] D. Perwitasari and Y. Ariati, “Model Prediksi Demam Berdarah Dengue Dengan Kondisi Iklim Di Kota Yogyakarta,” J. Ekol. Kesehat., vol. 14, no. 2, pp. 124–135, 2015.
[4] J. Mccall, “Genetic algorithms for modelling and optimisation,” vol. 184, pp. 205–222, 2005, doi: 10.1016/j.cam.2004.07.034.
[5] A. J. Mcmichael and E. Lindgren, “Climate change : present and future risks to health , and necessary responses,” pp. 401–413, 2011, doi: 10.1111/j.1365-2796.2011.02415.x.
[6] Nurbany, “Jumlah Penderita Meningkat, Bandung Jadi Kota Endemik DBD,” 2016. http://jabar.metrotvnews.com/read/2016/01/14/470533/jumlah-penderita-meningkat-bandung-jadi-kota-endemik-dbdmediacentre.
[7] S. Shaikh, Khalida; Memon, Khalida Naz; Sarah, Bibi; Akhtar, Rasheed; Memon, Manzoor; Memon, “Dengue Fever; An Audit Of Risk Factors Among Patients Reporting At A Tertiary Care Hospital In Hyderabad,” Prof. Med. J., vol. 21, no. 3, pp. 455–459, 2014.
[8] TH.Tedy, “Analisis faktor risiko perilaku masyarakat terhadap kejadian demam berdarah dengue (DBD) di kelurahan helvetia tengah medan tahun 2005,” J. Mutiara Kesehat. Indones., vol. 1, no. 2, pp. 42–47, 2005.
[9] R. Puspitasari and I. Susanto, “Analisis Spasial Kasus Demam Berdarah di Sukoharjo Jawa Tengah dengan Menggunakan Indeks Moran,” in Seminar Nasional Matematika dan Pendidikan Matematika, 2011, pp. 67–77.
[10] N. Latifah, E. Saraswati, and Prima Widayani, “Pemetaan Data Penyakit Menular Di Kota Semarang,” J. BUMI Indones., vol. 2, no. 2, pp. 162–171, 2013.
[11] M. G. Rosa-freitas, K. V Schreiber, P. Tsouris, E. Tatiani, D. S. Weimann, and J. F. Luitgards-moura, “Associations between dengue and combinations of weather factors in a city in the Brazilian Amazon,” Rev Panam Salud Publica/Pan Am J Public Heal., vol. 20, no. 4, 2006.
[12] Y. Yusof and Z. Mustaffa, “Dengue Outbreak Prediction : A Least Squares Support Vector Machines Approach,” Int. J. Comput. Theory Eng., vol. 3, no. 4, pp. 489–493, 2011.
[13] Y. Wu, G. Lee, X. Fu, and T. Hung, “Detect Climatic Factors Contributing to Dengue Outbreak based on Wavelet , Support Vector Machines and Genetic Algorithm,” in Proceedings of the World Congress on Engineering, 2008, vol. I.
[14] P. Eosina, T. Djatna, and H. Khusun, “A Cellular Automata Modeling for Visualizing and Predicting Spreading Patterns of Dengue Fever,” Telkomnika, vol. 14, no. 1, pp. 228–237, 2016, doi: 10.12928/TELKOMNIKA.v14i1.2404.
[15] H. Mulyani, T. Djatna, and I. S. Sitanggang, “Agent Based Modeling on Dynamic Spreading Dengue Fever Epidemic,” Telkomnika, vol. 15, no. 3, pp. 1380–1388, 2017, doi: 10.12928/TELKOMNIKA.v15i3.4511.
[16] M. P. Kalghatgi, M. Ramannavar, and N. S. Sidnal, “A Neural Network Approach to Personality Prediction based on the Big-Five Model,” vol. 2, no. 8, pp. 56–63, 2015.
[17] S. Chen, “Detection of fraudulent financial statements using the hybrid data mining approach,” Springerplus, vol. 5, no. 1, pp. 1–16, 2016, doi: 10.1186/s40064-016-1707-6.
[18] C. Zhang and H. Peng, “Application of A Self-adaption Dual Population Genetic Algorithm in Multi-objective Optimization Problems,” vol. 14, no. 1, pp. 238–244, 2016, doi: 10.12928/TELKOMNIKA.v14i1.2740.
[19] L. Helshani, “An Android Application for Google Map Navigation System , Solving the Travelling Salesman Problem , Optimization throught Genetic Algorithm The mathematical formulation throught graphs theory,” pp. 89–102, 2015.
[20] M. M. Navarro, “Evaluations of Crossover and Mutation Probability of Genetic Algorithm in an Optimal Facility Layout Problem,” pp. 3312–3317, 2016.
[21] Nahua Kang, Deep Learning for Rookies. © All Rights Reserved, 2017.
[22] Suyanto, Soft Computing: Membangun Mesin Ber-IQ Tinggi”. Bandung: Informatika, 2008.
[23] Y. Sibaroni, Fitriyani, and F. Nhita, “The Optimal High Performance Computing Infrastructure for Solving High Complexity Problem,” TELKOMNIKA, vol. 14, no. 4, pp. 281–288, 2016, doi: 10.12928/telkomnika.v14i4.
[24] P. Shrivastava and M. O’Mahony, “Design of Feeder Route Network Using Combined Genetic Algorithm and Specialized Repair Heuristic,” J. Public Transp., vol. 10, no. 2, pp. 109–133, 2007.
[25] P. Chakroborty and T. Dwivedi, “Optimal Route Network Design For Transit Systems Using Genetic Algorithms,” Eng. Optim., vol. 34, no. 1, pp. 83–100, 2002.
[26] E. Sadrsadat, H., Poorzahedi, H., Haghani, A., & Sharifi, “Bus Network Design Using Genetic Algorithm,” in 53rd annual transportation research forum, Tampa, Florida, 2012, pp. 1–16.
[27] R. Jain, S. Sontisirikit, S. Iamsirithaworn, and H. Prendinger, “Prediction of dengue outbreaks based on disease surveillance, meteorological and socio-economic data,” BMC Infect. Dis., vol. 19, no. 1, pp. 1–16, 2019, doi: 10.1186/s12879-019-3874-x.
Published
2020-11-21
How to Cite
SIBARONI, Yuliant; PRASETIYOWATI, Sri Suryani; SUDRAJAT, Iqbal Bahari.
Determination of dengue hemorrhagic fever disease factors using neural network and genetic algorithms.
Mathematical Sciences and Informatics Journal, [S.l.], v. 1, n. 2, p. 77-86, nov. 2020.
ISSN 2735-0703.
Available at: <https://myjms.mohe.gov.my/index.php/mij/article/view/14196>. Date accessed: 02 oct. 2023.
doi: https://doi.org/10.24191/mij.v1i2.14196.
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