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:: Volume 12, Issue 2 (1-2023) ::
JGST 2023, 12(2): 1-15 Back to browse issues page
Modeling and analysis of leishmaniasis distribution process using multilayer perceptron neural network and support vector regression (Case study: villages of Isfahan province)
Negar Shabanpour * , Neda Kaffash Charandabi , Mohammad Reza Shirzadi
Abstract:   (867 Views)
Villages located in Isfahan province are one of the areas prone to the spread of cutaneous leishmaniasis, which is characterized by the occurrence of wounds on the skin. To predict the future prevalence of cutaneous leishmaniasis, Continuous monitoring of the spatial distribution of this disease is essential. Disease modeling was performed using two machine learning algorithms called support vector regression (SVR) and multilayer perceptron neural network (MLP). The performance of these algorithms is evaluated using the RMSE index. Analysis of the results shows that SVR algorithm with RMSE = 0.170 compared to MLP with RMSE = 0.348 has better performance. Environmental factors include temperature, humidity, precipitation, altitude and wind speed as independent variables and Estimation of leishmaniasis density was used as a dependent variable in the modeling process, Of which (70%) were used for model training and the remaining (30%) for model evaluation. The results of spatial analysis index showed that The distribution pattern of cutaneous leishmaniasis in the years 1397 to 1399 was clustered.
Article number: 1
Keywords: Cutaneous leishmaniasis, Multilayer perceptron neural network, Support vector regression, Pattern of disease, Villages of Isfahan province
Full-Text [PDF 760 kb]   (475 Downloads)    
Type of Study: Research | Subject: GIS
Received: 2021/04/24
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shabanpour N, Kaffash Charandabi N, Shirzadi M R. Modeling and analysis of leishmaniasis distribution process using multilayer perceptron neural network and support vector regression (Case study: villages of Isfahan province). JGST 2023; 12 (2) : 1
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Volume 12, Issue 2 (1-2023) Back to browse issues page
نشریه علمی علوم و فنون نقشه برداری Journal of Geomatics Science and Technology