Modeling and analysis of leishmaniasis distribution process using multilayer perceptron neural network and support vector regression (Case study: villages of Isfahan province)

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.