Predictive risk mapping of human leptospirosis using support vector machine classification and multilayer perceptron neural network

  • Mehrdad Ahangarcani Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran, Islamic Republic of.
  • Mahdi Farnaghi | mahdi.farnaghi@nateko.lu.se Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran; GIS Center, Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden.
  • Mohammad Reza Shirzadi Center for Disease Control (CDC), Ministry of Health and Medical Education, Tehran, Iran, Islamic Republic of.
  • Petter Pilesjö GIS Center, Department of Physical Geography and Ecosystem Science, Lund University, Lund; Center for Middle-Eastern Studies, Lund University, Lund, Sweden.
  • Ali Mansourian GIS Center, Department of Physical Geography and Ecosystem Science, Lund University, Lund; Center for Middle-Eastern Studies, Lund University, Lund, Sweden.

Abstract

Leptospirosis is a zoonotic disease found wherever human is in direct or indirect contact with contaminated water and environment. Considering the increasing number of cases of this disease in the northern part of Iran, identifying areas characterized by high disease incidence risk can help policy-makers develop strategies to prevent its further spread. This study presents an approach for generating predictive risk maps of leptospirosis using spatial statistics, environmental variables and machine learning. Moran's I demonstrated that the distribution of leptospirosis cases in the study area in Iran was highly clustered. Pearson’s correlation analysis was conducted to examine the type and strength of relationships between climate and topographical factors and incidence of the disease. To handle the complex and nonlinear problems involved, machine learning based on the support vector machine classification algorithm and multilayer perceptron neural network was exploited to generate annual and monthly predictive risk maps of leptospirosis distribution. Performance of both models was evaluated using receiver operating characteristic curve and Kappa coefficient. The output results demonstrated that both models are adequate for the prediction of the probability of leptospirosis incidence.

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Published
2019-05-14
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Original Articles
Keywords:
Leptospirosis, Geographical information systems, Support vector machine, Multilayer perceptron neural network, Geostatistics, Iran
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How to Cite
Ahangarcani, M., Farnaghi, M., Shirzadi, M. R., Pilesjö, P., & Mansourian, A. (2019). Predictive risk mapping of human leptospirosis using support vector machine classification and multilayer perceptron neural network. Geospatial Health, 14(1). https://doi.org/10.4081/gh.2019.711