Construction of a genetic method to forecast the population health indicators based on neural network models

Authors

DOI:

https://doi.org/10.15587/1729-4061.2020.197319

Keywords:

neural networks, genetic algorithm, phenotype, modified genetic mutation operator, forecasting of public health indicators

Abstract

A genetic method has been proposed to forecast the health indicators of population based on neural-network models. The fundamental difference of the proposed genetic method from existing analogs is the use of the diploid set of chromosomes in individuals in a population that is evolving. Such modification makes the dependence of the phenotype of the individual on the genotype less deterministic and, ultimately, helps preserve the diversity of the gene pool of the population and the variability of features of the phenotype during the execution of the algorithm. In addition, a modification of the genetic operator of mutations has been proposed. In addition, a modification genetic operator of mutations is proposed. In contrast to the classical method, those individuals that are exposed to the operator of mutations are selected not randomly but according to their mutation resistance corresponding to the value of the function of an individual adaptability. Thus, individuals with worse values of the target function are mutated, and the genome of the strong individuals remains unchanged. In this case, the likelihood of loss of the function reached during the evolution of the extremum due to the action of the mutation operator decreases, and the transition to the new extremum occurs if enough specific weight of the best attributes in the population is accumulated.

A comparative analysis of the models synthesized with the help of the developed genetic method has shown that the best results were achieved in the model based on a neural network of long short-term memory. While creating and training the model based on a long short-term network, the ability to use the particle swarm method to optimize the network settings was investigated. The results of our experimental study have shown that the developed model yields the smallest error in predicting the number of new cases of tuberculosis – the average absolute error is 6.139, which is less compared with models that were built by using other methods).

The practical application of the developed methods would make it possible to timely adjust the planned treatment and diagnostic, preventive measures, to determine in advance the necessary resources for localization and elimination of diseases in order to maintain people's health.

Author Biographies

Ievgen Fedorchenko, Zaporizhzhia Polytechnic National University Zhukovskoho str., 64, Zaporizhzhia, Ukraine, 69063

Senior Lecturer

Department of Software Tools

Andrii Oliinyk, Zaporizhzhia Polytechnic National University Zhukovskoho str., 64, Zaporizhzhia, Ukraine, 69063

PhD, Associate Professor

Department of Software Tools

Alexander Stepanenko, Zaporizhzhia Polytechnic National University Zhukovskoho str., 64, Zaporizhzhia, Ukraine, 69063

PhD, Associate Professor

Department of Software Tools

Tetiana Zaiko, Zaporizhzhia Polytechnic National University Zhukovskoho str., 64, Zaporizhzhia, Ukraine, 69063

PhD, Associate Professor

Department of Software Tools

Serhii Korniienko, Zaporizhzhia Polytechnic National University Zhukovskoho str., 64, Zaporizhzhia, Ukraine, 69063

PhD, Associate Professor

Department of Software Tools

Anastasiia Kharchenko

Software Developer

References

  1. Paustenbach, D. (Ed.) (2002). Paustenbach Human and ecological risk assessment. Theory and practice. New York, 635.
  2. Vykydy zabrudniuiuchykh rechovyn v atmosferne povitria. Derzhavna sluzhba statystyky Ukrainy. Available at: https://ukrstat.org/uk/operativ/operativ2009/ns_rik/ns_u/dvsr_u2008.html
  3. Stan zabrudnennia pryrodnoho seredovyshcha na terytoriyi Ukrainy. Available at: http://cgo-sreznevskyi.kiev.ua/index.php?fn=u_zabrud&f=ukraine
  4. Tuberculosis. World Health Organization. Available at: https://www.who.int/news-room/fact-sheets/detail/tuberculosis
  5. Ghazvini, K., Yousefi, M., Firoozeh, F., Mansouri, S. (2019). Predictors of tuberculosis: Application of a logistic regression model. Gene Reports, 17, 100527. doi: https://doi.org/10.1016/j.genrep.2019.100527
  6. Mei, B., Xu, Y. (2019). Multi-task least squares twin support vector machine for classification. Neurocomputing, 338, 26–33. doi: https://doi.org/10.1016/j.neucom.2018.12.079
  7. Rubal, Kumar, D. (2018). Evolving Differential evolution method with random forest for prediction of Air Pollution. Procedia Computer Science, 132, 824–833. doi: https://doi.org/10.1016/j.procs.2018.05.094
  8. Dembinski, H., Schmelling, M., Waldi, R. (2019). Application of the iterated weighted least-squares fit to counting experiments. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 940, 135–141. doi: https://doi.org/10.1016/j.nima.2019.05.086
  9. Soebiyanto, R. P., Kiang, R. K. (2000). Modeling Influenza Transmission Using Environmental Parameters. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science, XXXVIII, 330–334.
  10. Yi, H.-C., You, Z.-H., Zhou, X., Cheng, L., Li, X., Jiang, T.-H., Chen, Z.-H. (2019). ACP-DL: A Deep Learning Long Short-Term Memory Model to Predict Anticancer Peptides Using High-Efficiency Feature Representation. Molecular Therapy - Nucleic Acids, 17, 1–9. doi: https://doi.org/10.1016/j.omtn.2019.04.025
  11. Speiser, J. L., Miller, M. E., Tooze, J., Ip, E. (2019). A comparison of random forest variable selection methods for classification prediction modeling. Expert Systems with Applications, 134, 93–101. doi: https://doi.org/10.1016/j.eswa.2019.05.028
  12. Alam, S., Dobbie, G., Koh, Y. S., Riddle, P., Ur Rehman, S. (2014). Research on particle swarm optimization based clustering: A systematic review of literature and techniques. Swarm and Evolutionary Computation, 17, 1–13. doi: https://doi.org/10.1016/j.swevo.2014.02.001
  13. Kumar, J., Goomer, R., Singh, A. K. (2018). Long Short Term Memory Recurrent Neural Network (LSTM-RNN) Based Workload Forecasting Model For Cloud Datacenters. Procedia Computer Science, 125, 676–682. doi: https://doi.org/10.1016/j.procs.2017.12.087
  14. Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. O'Reilly Media, Inc.
  15. Viktorova, О. (2012). Use of fuzzy neural networks for technical diagnostics of road machinery. Vestnik Har'kovskogo natsional'nogo avtomobil'no-dorozhnogo universiteta, 56, 98–102.
  16. McClure, N. (2017). TensorFlow Machine Learning Cookbook. Packt Publishing, 370.
  17. Kolesnikov, K. V., Karapetian, A. R., Tsarenko, T. A. (2013). Henetychni alhorytmy dlia zadach bahatokryterialnoi optymizatsii v merezhakh adaptyvnoi marshrutyzatsiyi danykh. Visnyk Nats. tekhn. un-tu "KhPI", 56 (1029), 44–50.
  18. Oliinyk, A., Fedorchenko, I., Stepanenko, A., Rud, M., Goncharenko, D. (2018). Evolutionary Method for Solving the Traveling Salesman Problem. 2018 International Scientific-Practical Conference Problems of Infocommunications. Science and Technology (PIC S&T). doi: https://doi.org/10.1109/infocommst.2018.8632033
  19. Lin, B., Sun, X., Salous, S. (2016). Solving Travelling Salesman Problem with an Improved Hybrid Genetic Algorithm. Journal of Computer and Communications, 04 (15), 98–106. doi: https://doi.org/10.4236/jcc.2016.415009
  20. Haupt, R. L., Haupt, S. E. (2003). Practical Genetic Algorithms. John Wiley & Sons. doi: https://doi.org/10.1002/0471671746
  21. Shkarupylo, V., Skrupsky, S., Oliinyk, A., Kolpakova, T. (2017). Development of stratified approach to software defined networks simulation. Eastern-European Journal of Enterprise Technologies, 5 (9 (89)), 67–73. doi: https://doi.org/10.15587/1729-4061.2017.110142
  22. Fedorchenko, I., Oliinyk, A., Stepanenko, A., Zaiko, T., Shylo, S., Svyrydenko, A. (2019). Development of the modified methods to train a neural network to solve the task on recognition of road users. Eastern-European Journal of Enterprise Technologies, 2 (9 (98)), 46–55. doi: https://doi.org/10.15587/1729-4061.2019.164789
  23. Oliinyk, A., Zaiko, T., Subbotin, S. (2014). Training sample reduction based on association rules for neuro-fuzzy networks synthesis. Optical Memory and Neural Networks, 23 (2), 89–95. doi: https://doi.org/10.3103/s1060992x14020039
  24. Fedorchenko, I., Oliinyk, A., Stepanenko, A., Zaiko, T., Korniienko, S., Burtsev, N. (2019). Development of a genetic algorithm for placing power supply sources in a distributed electric network. Eastern-European Journal of Enterprise Technologies, 5 (3 (101)), 6–16. doi: https://doi.org/10.15587/1729-4061.2019.180897
  25. Fedorchenko, I., Oliinyk, A., Stepanenko, A., Zaiko, T., Shylo, S., Svyrydenko, A. (2019). Development of the modified methods to train a neural network to solve the task on recognition of road users. Eastern-European Journal of Enterprise Technologies, 2 (9 (98)), 46–55. doi: https://doi.org/10.15587/1729-4061.2019.164789
  26. Oliinyk, A. O., Zayko, T. A., Subbotin, S. O. (2014). Synthesis of Neuro-Fuzzy Networks on the Basis of Association Rules. Cybernetics and Systems Analysis, 50 (3), 348–357. doi: https://doi.org/10.1007/s10559-014-9623-7
  27. Oliinyk, A., Fedorchenko, I., Stepanenko, A., Rud, M., Goncharenko, D. (2019). Combinatorial Optimization Problems Solving Based on Evolutionary Approach. 2019 IEEE 15th International Conference on the Experience of Designing and Application of CAD Systems (CADSM). doi: https://doi.org/10.1109/cadsm.2019.8779290
  28. Sharifzadeh, M., Sikinioti-Lock, A., Shah, N. (2019). Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression. Renewable and Sustainable Energy Reviews, 108, 513–538. doi: https://doi.org/10.1016/j.rser.2019.03.040
  29. Buduma, N., Locascio, N. (Eds.) (2017). Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms. O'Reilly Media, 298.
  30. Lapan, M. (2018). Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more. Packt Publishing, 546.
  31. Aggarwal, C. C. (2018). Neural Networks and Deep Learning. Springer. doi: https://doi.org/10.1007/978-3-319-94463-0

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Published

2020-02-29

How to Cite

Fedorchenko, I., Oliinyk, A., Stepanenko, A., Zaiko, T., Korniienko, S., & Kharchenko, A. (2020). Construction of a genetic method to forecast the population health indicators based on neural network models. Eastern-European Journal of Enterprise Technologies, 1(4 (103), 52–63. https://doi.org/10.15587/1729-4061.2020.197319

Issue

Vol. 1 No. 4 (103) (2020): Mathematics and Cybernetics - applied aspects

Section

Mathematics and Cybernetics - applied aspects

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Copyright (c) 2020 Ievgen Fedorchenko, Andrii Oliinyk, Alexander Stepanenko, Tetiana Zaiko, Serhii Korniienko, Anastasiia Kharchenko

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