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Application of artificial neural network in medical geochemistry

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Abstract

For the evaluation of various adverse health effects of chemical elements occurring in the environment on humans, the comparison and linking of geochemical data (chemical composition of groundwater, soils, and dusts) with data on health status of population (so-called health indicators) play a key role. Geochemical and health data are predominantly nonlinear, and the use of standard statistical methods can lead to wrong conclusions. For linking such data, we find appropriate the use method of artificial neural networks (ANNs) which enable to eliminate data inhomogeneity and also potential data errors. Through method of ANNs, we are able to determine the order of influence of chemical elements on health indicators as well as to define limit values for the influential elements at which the health status of population is the most favourable (i.e. the lowest mortality, the highest life expectancy). For determination of dependence between the groundwater contents of chemical elements and health indicators, we recommend to create 200 ANNs. In further calculations performed for identification of order of influence of chemical elements as well as definition of limit values, we propose to work with median or mean values from calculated 200 ANNs. The ANN represents an appropriate method to be used for environmental and health data analysis in medical geochemistry.

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References

  • Beaglehole, R., Bonita, R., & Kjellstrom, T. (1993). Basic epidemiology. Geneva: World Health Organization.

    Google Scholar 

  • Bencko, V., Hrach, K., Malý, M., Pikhart, H., Reissigová, J., Svačina, Š., et al. (2003a). Biomedicínska statistika III., Statistické metody v epidemiologii (1) (p. s. 236). Praha: Nakladatelství Karolinum. (in Czech). ISBN 80-246-0763-8.

    Google Scholar 

  • Bencko, V., Hrach, K., Malý, M., Pikhart, H., Reissigová, J., Svačina, Š., et al. (2003b). Biomedicínska statistika III., Statistické metody v epidemiologii (2) (p. s. 269). Praha: Nakladatelství Karolinum. (in Czech). ISBN 80-246-0764-6.

    Google Scholar 

  • Chaikaew, N., Tripathi, N. K., & Souris, M. (2009). Exploring spatial patterns and hotspots of diarrhea in Chiang Mai, Thailand. International Journal of Health Geographics. doi:10.1186/1476-072X-8-3.

    Google Scholar 

  • Cheh, J. J., Weinberg, R. S., & Yook, K. C. (2013). An application of an artificial neural network investment system to predict takeover targets. Journal of Applied Business Research (JABR), 15(4), 33–46.

    Article  Google Scholar 

  • Chen, J., Roth, R. E., Naito, A. T., Lengerich, E. J., & MacEachren, A. M. (2008). Geovisual analytics to enhance spatial scan statistic interpretation: An analysis of US cervical cancer mortality. International Journal of Health Geographics, 7(1), 57.

    Article  Google Scholar 

  • Cvečková, V., Fajčíková, K., & Rapant, S. (2016). Geohealth (p. 92). Bratislava: Monograph, State geological Institute of Dionyz Stur. ISBN 978-80-8174-017-6.

    Google Scholar 

  • Fischer, M. M., & Nijkamp, P. (Eds.). (1993). Geographic information systems, spatial modelling and policy evaluation (p. 280). Berlin, Heidelberg: Springer-Verlag.

    Google Scholar 

  • Gevrey, M., Dimopoulos, I., & Lek, S. (2003). Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecological Modelling, 160, 249–264.

    Article  Google Scholar 

  • Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Network, 2, 359–366.

    Article  Google Scholar 

  • Hunter, A., Kennedy, L., Henry, J., & Fergusson, I. (2000). Application of neural networks and sensitivity analysis to improved prediction of trauma survival. Computer Methods and Programs in Biomedicine, 62(1), 11–19.

    Article  CAS  Google Scholar 

  • Jammazi, R., & Aloui, C. (2012). Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling. Energy Economics, 34(3), 828–841.

    Article  Google Scholar 

  • Jenicek, M. (1995). Epidemiology, the logic of modern medicine. Montreal: Epimed. ISBN 0-9698912-0-2.

    Google Scholar 

  • Kaytez, F., Taplamacioglu, M. C., Cam, E., & Hardalac, F. (2015). Forecasting electricity consumption: a comparison of regression analysis, neural networks and least squares support vector machines. International Journal of Electrical Power & Energy Systems, 67, 431–438.

    Article  Google Scholar 

  • Klinda, J., & Lieskovská, Z. (2010). State of the environment report of the Slovak Republic (p. 192). Bratislava: Ministry of Environment of the Slovak Republic.

    Google Scholar 

  • Kovalishyn, V. V., Tetko, I. V., Luik, A. I., Kholodovych, V. V., Villa, A. E. P., & Livingstone, D. J. (1998). Neural network studies. 3. Variable selection in the cascade-correlation learning architecture. Journal of Chemical Information and Computer Sciences, 38, 651–659.

    Article  CAS  Google Scholar 

  • Kriesel, D. (2007). Ein kleiner Überblick über Neuronale Netze (p. 238). Bonn: Rheinische Friedrich-Wilhelms Universität Bonn.

    Google Scholar 

  • Last, J. M. (2001). A Dictionary of epidemiology. Oxford: Oxford University Press. ISBN 0-19-514169-5.

    Google Scholar 

  • Maclin, R., & Opitz, D. (2011). Popular ensemble methods: An empirical study. Journal Of Artificial Intelligence Research, 11, 169–198. https://arxiv.org/abs/1106.0257.

  • Maier, H. R., Morgan, N., & Chow, C. W. (2004). Use of artificial neural networks for predicting optimal alum doses and treated water quality parameters. Environmental Modelling and Software, 19(5), 485–494.

    Article  Google Scholar 

  • McClelland, J. L., & Rumelhart, D. E. (1987). Parallel distributed processing: Explorations in the microstructure of cognition, psychological and biological models 2 (p. 632). Cambridge, MA: A Bradford Book, MIT Press.

    Google Scholar 

  • McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, 115–133.

    Article  Google Scholar 

  • Nakamura, E. (2005). Inflation forecasting using a neural network. Economics Letters, 86(3), 373–378.

    Article  Google Scholar 

  • Rapant, S., Vrana, K., & Bodiš, D. (1996). Geochemical Atlas of Slovakia-part I. Groundwater. Monograph, Ministry of Environment of the Slovak Republic, Geological Survey of Slovak Republic, Bratislava, p. 127.

  • Rapant, S., Rapošová, M., Bodiš, D., Marsina, K., & Slaninka, I. (1999). Environmental-geochemical mapping program in the Slovak Republic. Journal of Geochemical Exploration, 66(2), 151–158.

    Article  CAS  Google Scholar 

  • Rapant, S., Letkovičová, M., Cvečková, V., Fajčíková, K., Galbavý, J., & Letkovič, M. (2010). Environmental and health indicators of the Slovak Republic. Monograph, SGIDŠ Bratislava, p. 279. (in Slovak).

  • Rapant, S., Cvečková, Veronika, Dietzová, Z., Fajčíková, K., Hiller, E., Finkelman, R. B., et al. (2014). The potential impact of geological environment on health status of residents of the Slovak Republic. Environmental Geochemistry and Health, 36, 543–561.

    Article  CAS  Google Scholar 

  • Rapant, S., Fajčíková, K., Cvečková, V., Ďurža, A., Stehlíková, B., Sedláková, D., et al. (2015). Chemical composition of groundwater and relative mortality for cardiovascular diseases in the Slovak Republic. Environmental Geochemistry and Health, 37, 745–756.

    Article  CAS  Google Scholar 

  • Rapant, S., Cvečková, V., Fajčíková, K., Dietzová, Z., & Stehlíková, B. (2016). Chemical composition of groundwater/drinking water and oncological disease mortality, Slovak Republic. Environmental Geochemistry and Health. doi:10.1007/s10653-016-9820-6.

    Google Scholar 

  • Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65, 386–408.

    Article  CAS  Google Scholar 

  • Rosenblatt, F. (1962). Principles of neurodynamics: Perceptrons and the theory of brain machines (p. 616). Washington: Spartan Books.

    Google Scholar 

  • Rovithakis, G. A., & Christodoulou, M. A. (2012). Adaptive control with recurrent high-order neural networks: theory and industrial applications. New York: Springer Science and Business Media.

    Google Scholar 

  • Rumelhart, D. E., & McClelland, J. L. (1986). Parallel distributed processing: Explorations in the microstructure of cognition, foundations 1 (p. 567). Cambridge, MA: MIT Press.

    Google Scholar 

  • Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986a). Learning internal representations by error propagation. In D. E. Rumelhart & J. L. McClelland (Eds.), Parallel distributed processing: explorations in the microstructure of cognition 1. Cambridge, MA: MIT Press.

    Google Scholar 

  • Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986b). Learning representations by back-propagating errors. Nature, 323, 533–536.

    Article  Google Scholar 

  • Sahoo, G. B., Ray, C., Mehnert, E., & Keefer, D. A. (2006). Application of artificial neural networks to assess pesticide contamination in shallow groundwater. Science of the Total Environment, 367(1), 234–251.

    Article  CAS  Google Scholar 

  • Singh, R. M., Datta, B., & Jain, A. (2004). Identification of unknown groundwater pollution sources using artificial neural networks. Journal of water resources planning and management, 130(6), 506–514.

    Article  Google Scholar 

  • StatSoft. (1999). Electronic statistics textbook. (On-line manual), http://www.statsoft.com/textbook/statistics-glossary/s/button/s/.

  • Vrana, K., Rapant, S., Bodiš, D., Marsina, K., Lexa, J., Pramuka, S., et al. (1997). Geochemical atlas of Slovak Republic at a scale 1: 1 000 000. Journal of Geochemical Exploration, 60, 7–37.

    Article  Google Scholar 

  • www.geology.sk/geohealth. Accessed 15 Nov 2016.

  • www.who.int. Accessed 24 Nov 2016.

  • www.who.int/classifications/icd/en/. Accessed 24 Nov 2016.

  • www.statistics.sk. Accessed 20 Oct 2016.

  • Yan, S., & Minsker, B. (2006).Optimal groundwater remediation design using an adaptive neural network genetic algorithm. Water Resources Research, 42(5). doi:10.1029/2005WR004303

  • Zhou, Z.-H. (2012). Ensemble methods: foundations and algorithms. A Chapman & Hall Book, Taylor & Francis group, CRC Press, p. 234 https://www.islab.ntua.gr/attachments/article/86/Ensemble%20methods%20-%20Zhou.pdf.

  • Zurada, J. M., Eberhart, R. C., & Cloete, I. (1995). Determining the Significance of Input Parameters Using Sensitivity Analysis. Lecture Notes Computer Science, 930, 382–388.

    Article  Google Scholar 

Download references

Acknowledgements

This research has been performed within the Projects Geohealth (LIFE10 ENV/SK/000086) and Life for Krupina (LIFE12 ENV/SK/000094) which are financially supported by the EU’s funding instrument for the environment: Life + programme and Ministry of the Environment of the Slovak Republic. We thank Robert Finkelman for constructive comments that improved the manuscript.

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Authors and Affiliations

  1. State Geological Institute of Dionyz Stur, Mlynská dolina 1, 817 04, Bratislava, Slovak Republic

    K. Fajčíková, V. Cvečková & S. Rapant

  2. Faculty of Economics and Business, Paneuropean University, Tematínska 10, 851 05, Bratislava, Slovak Republic

    B. Stehlíková

Authors
  1. K. Fajčíková
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  2. B. Stehlíková
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  3. V. Cvečková
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  4. S. Rapant
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Correspondence to K. Fajčíková.

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Fajčíková, K., Stehlíková, B., Cvečková, V. et al. Application of artificial neural network in medical geochemistry. Environ Geochem Health 39, 1513–1529 (2017). https://doi.org/10.1007/s10653-017-9944-3

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  • DOI: https://doi.org/10.1007/s10653-017-9944-3

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