02657nas a2200241   4500000000100000008004100001260001600042653004300058653003200101653002800133653003200161100001700193700002100210700002200231700001100253700001400264245012300278856015300401300001100554490000800565520182800573022001402401       2022                            d  bElsevier BV10aManagement, Monitoring, Policy and Law10aComputers in Earth Sciences10aEarth-Surface Processes10aGlobal and Planetary Change1 aShabanpour N1 aRazavi-Termeh SV1 aSadeghi-Niaraki A1 aChoi S1 aAbuhmed T00aIntegration of machine learning algorithms and GIS-based approaches to cutaneous leishmaniasis prevalence risk mapping  uhttps://www.sciencedirect.com/science/article/pii/S1569843222000565/pdfft?md5=83218302ec4b417dad7e7e729483b00b&pid=1-s2.0-S1569843222000565-main.pdf  a1028540 v1123 a<p>Cutaneous leishmaniasis is a complex infection that is caused by different species of Leishmania and affects more than 2 million people in 88 countries. Identifying the environmental factors affecting the occurrence of cutaneous leishmaniasis and preparing a risk map is one of the basic tools to control and manage this disease. The aim of this study was a spatial prediction of cutaneous leishmaniasis in Isfahan province, Iran using three machine learning algorithms (decision tree (DT), support vector regression (SVR), and linear regression (LR)). The spatial database was created using data collected on the number of diseases in Isfahan province from 2011 to 2018, as well as ten environmental parameters (temperature, humidity, rainfall, altitude, slope, wind speed,&nbsp;<a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/normalized-difference-vegetation-index">normalized difference vegetation index</a>&nbsp;(NDVI), number of sunny days, number of frosty days, and distance to stream) that affect the incidence of leishmaniasis. Furthermore, the fuzzy method was employed in this study to reduce uncertainty and evaluate the effect of environmental factors on disease prevalence. Using the holdout method and 70:30 ratios, the data were used to model and prepare a leishmaniasis prediction map and evaluate the results, respectively. The accuracy of the maps satisfied with the DT, SVR, and LR algorithms was 0.951, 0.934, and 0.914, respectively, according to the receiver operating characteristic (ROC) curve and area under the curve (AUC). Furthermore, the eastern and southern parts of the province have the lowest risk of leishmaniasis. The result of this issue is the identification of high-risk areas of the disease and increase life and peace for people in the community.</p>
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