Integration of machine learning algorithms and GIS-based approaches to cutaneous leishmaniasis prevalence risk mapping
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 con...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-08-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843222000565 |
| _version_ | 1817999313843781632 |
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| author | Negar Shabanpour Seyed Vahid Razavi-Termeh Abolghasem Sadeghi-Niaraki Soo-Mi Choi Tamer Abuhmed |
| author_facet | Negar Shabanpour Seyed Vahid Razavi-Termeh Abolghasem Sadeghi-Niaraki Soo-Mi Choi Tamer Abuhmed |
| author_sort | Negar Shabanpour |
| collection | DOAJ |
| description | 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, normalized difference vegetation index (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. |
| first_indexed | 2024-04-14T03:07:02Z |
| format | Article |
| id | doaj.art-64011d6e60d34719be9e17c821d9550c |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-04-14T03:07:02Z |
| publishDate | 2022-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-64011d6e60d34719be9e17c821d9550c2022-12-22T02:15:42ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322022-08-01112102854Integration of machine learning algorithms and GIS-based approaches to cutaneous leishmaniasis prevalence risk mappingNegar Shabanpour0Seyed Vahid Razavi-Termeh1Abolghasem Sadeghi-Niaraki2Soo-Mi Choi3Tamer Abuhmed4Geoinformation Tech. Center of Excellence, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran 19697, IranGeoinformation Tech. Center of Excellence, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran 19697, IranDepartment of Computer Science and Engineering & Convergence Engineering for Intelligent Drone, Sejong University, Seoul 143-747, Republic of Korea; Corresponding author.Department of Computer Science and Engineering & Convergence Engineering for Intelligent Drone, Sejong University, Seoul 143-747, Republic of KoreaCollege of Computing and Informatics, Sungkyunkwan University, Suwon 16419, Republic of KoreaCutaneous 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, normalized difference vegetation index (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.http://www.sciencedirect.com/science/article/pii/S1569843222000565Cutaneous leishmaniasisMachine learningSpatial modellingEnvironmental factorsHealth geography |
| spellingShingle | Negar Shabanpour Seyed Vahid Razavi-Termeh Abolghasem Sadeghi-Niaraki Soo-Mi Choi Tamer Abuhmed Integration of machine learning algorithms and GIS-based approaches to cutaneous leishmaniasis prevalence risk mapping International Journal of Applied Earth Observations and Geoinformation Cutaneous leishmaniasis Machine learning Spatial modelling Environmental factors Health geography |
| title | Integration of machine learning algorithms and GIS-based approaches to cutaneous leishmaniasis prevalence risk mapping |
| title_full | Integration of machine learning algorithms and GIS-based approaches to cutaneous leishmaniasis prevalence risk mapping |
| title_fullStr | Integration of machine learning algorithms and GIS-based approaches to cutaneous leishmaniasis prevalence risk mapping |
| title_full_unstemmed | Integration of machine learning algorithms and GIS-based approaches to cutaneous leishmaniasis prevalence risk mapping |
| title_short | Integration of machine learning algorithms and GIS-based approaches to cutaneous leishmaniasis prevalence risk mapping |
| title_sort | integration of machine learning algorithms and gis based approaches to cutaneous leishmaniasis prevalence risk mapping |
| topic | Cutaneous leishmaniasis Machine learning Spatial modelling Environmental factors Health geography |
| url | http://www.sciencedirect.com/science/article/pii/S1569843222000565 |
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