Document Type : Original Article(s)
Authors
1
Associate Professor, Department of Environmental Health Engineering, School of Health, North Khorasan University of Medical Sciences
2
Instructor, Department of Environmental Health, School of Health and Non-Communicable Diseases Research Center, Sabzevar University of Medical Sciences, Sabzevar, Iran
3
Medical student, Student Research Committee, Sabzevar University of Medical Sciences, Sabzevar, Iran
4
Assistant Professor, Leishmaniasis Research Center, Sabzevar University of Medical Sciences, Sabzevar, Iran
5
Associate Professor, Leishmaniasis Research Center, Sabzevar University of Medical Sciences, Sabzevar, Iran.
10.48305/jims.2026.45129.2412
Abstract
Background: Cutaneous leishmaniasis (CL) is a parasitic and zoonotic disease. This study compares three methods: Adaptive Neuro-Fuzzy Inference System (ANFIS), Multi-Layer Perceptron (MLP), and Principal Component Analysis (PCA-ANFIS) for modeling and predicting this disease in Sabzevar County.
Methods: The data used included CL cases (2014–2021) and influencing factors such as population, climate, vegetation, and topography. GIS (Geographic Information System) and ANFIS, MLP, and PCA-ANFIS models were employed for the spatial prediction of the disease.
Findings: The highest incidence rates occurred in autumn and winter, while spring had the lowest. Normalized Difference Vegetation Index (NDVI) had the most significant impact on disease prediction. The ANFIS model predicted the highest number of cases in the Beyhaq district. The MLP model showed similar results but predicted fewer cases in the Zarrin, Foroughan, and Rob'e Shammat areas. The PCA-ANFIS model indicated higher incidence rates in areas such as Hokmabad, Karraab, and Robat compared to the other two models. The predicted risk of disease was similar across all three models.
Conclusion: The PCA-ANFIS model performed better in predicting cutaneous leishmaniasis in Sabzevar. This study can contribute to the preparation of disease prediction and vulnerability maps and provide valuable information for health planning and reducing the prevalence of leishmaniasis.
Highlights
Ayoob Rastegar:Google Scholar, PubMed
Ali Oghazyan: Google Scholar, PubMed
Ali Pouryousef: Google Scholar, PubMed
Mohammad-Shafi Mojadadi: Google Scholar, PubMed
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