Document Type : Review article
Authors
1 Department of Mathematical Sciences, Universiti Teknologi Malaysia, Johor, Malaysia /Department of Information Technology, Modibbo Adama University of Technology, Yola School of Management and Information Technology, Adamawa State, Nigeria
2 Department of Mathematical Sciences, Universiti Teknologi Malaysia, Johor, Malaysia
3 Adekunle Ajasin University, Department of Mathematical Sciences, Faculty of Science, Ondo State, Nigeria
Abstract
Background and aims: Since accurate forecasts help inform decisions for preventive health-care
intervention and epidemic control, this goal can only be achieved by making use of appropriate
techniques and methodologies. As much as forecast precision is important, methods and model
selection procedures are critical to forecast precision. This study aimed at providing an overview of
the selection of the right artificial neural network (ANN) methodology for the epidemic forecasts. It is
necessary for forecasters to apply the right tools for the epidemic forecasts with high precision.
Methods: It involved sampling and survey of epidemic forecasts based on ANN. A comparison of
performance using ANN forecast and other methods was reviewed. Hybrids of a neural network with
other classical methods or meta-heuristics that improved performance of epidemic forecasts were
analysed.
Results: Implementing hybrid ANN using data transformation techniques based on improved
algorithms, combining forecast models, and using technological platforms enhance the learning and
generalization of ANN in forecasting epidemics.
Conclusion: The selection of forecasting tool is critical to the precision of epidemic forecast; hence, a
working guide for the choice of appropriate tools will help reduce inconsistency and imprecision in
forecasting epidemic size in populations. ANN hybrids that combined other algorithms and models,
data transformation and technology should be used for an epidemic forecast.
Keywords
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22. Sharma S. Role of Data Mining Techniques in Human Disease Diagnosis Abstract. Int J Comput Technol. 2016;3(2):184-8.
23. Faisal T, Taib MN, Ibrahim F. Neural network diagnostic system for dengue patients risk classification. J Med Syst. 2012;36(2):661-76. doi: 10.1007/s10916-010-9532-x.
24. Hwang S, Clarite DS, Elijorde FI, Gerardo BD, Byun Y. A web-based analysis for dengue tracking and prediction using artificial neural network. Adv Sci Technol Lett. 2016;122:160- 4. doi: 10.14257/astl.2016.122.32.
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26. Jabbar MA, Deekshatulu BL, Chandra P. Classification of heart disease using artificial neural network and feature subset selection. Global Journal of Computer Science and Technology Neural & Artificial Intelligence. 2013;13(3):4-8.
27. Er O, Yumusak N, Temurtas F. Chest diseases diagnosis using artificial neural networks. Expert Syst Appl. 2010;37(12):7648- 55. doi: 10.1016/j.eswa.2010.04.078.
28. Ghosh D, Guha R. Use of genetic algorithm and neural network approaches for risk factor selection: A case study of West Nile virus dynamics in an urban environment. Comput Environ Urban Syst. 2010;34(3):189-203. doi: 10.1016/j. compenvurbsys.2010.02.007.
29. Elveren E, Yumusak N. Tuberculosis disease diagnosis using artificial neural network trained with genetic algorithm. J Med Syst. 2011;35(3):329-32. doi: 10.1007/s10916-009-9369-3.
30. Priya E, Srinivasan S. Automated Identification of Tuberculosis Objects in Digital Images Using Neural Network and Neuro Fuzzy Inference Systems. J Med Imaging Heal Informatics [Internet]. 2015;5(3):506–12. Available from: http://openurl. ingenta.com/content/xref?genre=article&issn=2156- 7018&volume=5&issue=3&spage=506
31. Muller PS, Meenakshi Sundaram S, Nirmala M, Nagarajan E. Application of computational technique in design of classifier for early detection of gestational diabetes mellitus. Appl Math Sci. 2015;9(67):3327-36. doi: 10.12988/ams.2015.54319.
32. Biswas SK. An ANN Based Pattern Classification Algorithm for Diagnosis of Swine Flu. Journal of Intelligent Computing. 2014;5(1):16-30. 33.
33. Sanoob MU, Madhu A, Ajesh KR, Varghese SM. Artificial neural network for diagnosis of pancreatic cancer. Int J Cybern Inform. 2016;5(2):41-9. doi: 10.5121/ijci.2016.5205.
34. Breu F, Guggenbichler S, Wollmann J. Utilization of Neural Network for Disease Forecasting. Vasa [Internet]. 2008; Available from: http://medcontent.metapress.com/index/ A65RM03P4874243N.pdf.
35. Baridam B, Irozuru C. The Prediction of Prevalence and Spread of HIV/AIDS Using Artificial Neural Network–the Case of Rivers State in the Niger Delta, Nigeria. Int J Comput Appl. 2012;44(2):42-5.
36. Prieto A, Prieto B, Ortigosa EM, Ros E, Pelayo F, Ortega J, et al. Neural networks: An overview of early research, current frameworks and new challenges. Neurocomputing. 2016;214:242-68. doi: 10.1016/j.neucom.2016.06.014.
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38. Pasini A. Artificial neural networks for small dataset analysis. J Thorac Dis. 2015;7(5):953-60. doi: 10.3978/j.issn.2072- 1439.2015.04.61.
39. Livingstone D. A Practical Guide to Scientific Data Analysis. UK: John Wiley & Sons, Ltd; 2009. doi: 10.1002/9780470017913.
40. Aburas HM, Cetiner BG, Sari M. Dengue confirmed-cases prediction: A neural network model. Expert Syst Appl. 2010;37(6):4256-60. doi: 10.1016/j.eswa.2009.11.077.
41. Husin NA, Mustapha N, Sulaiman MN, Yaakob R. A hybrid model using genetic algorithm and neural network for predicting dengue outbreak. In: 2012 4th Conference on Data Mining and Optimization (DMO). Langkawi, Malaysia: IEEE; 2012. p. 23-7. doi: 10.1109/DMO.2012.6329793.
42. Maier HR, Dandy GC, May RJ, Fernando T. Efficient selection of inputs for artificial neural network models. MODSIM 2005 Int Congr Model Simul; 2005;170–6. Available from: http:// www.mssanz.org.au/modsim05/papers/fernando.pdf.
43. Karlik B, Vehbi Olgac A. Performance analysis of various activation functions in generalized MLP architectures of neural networks. International Journal of Artificial Intelligence and Expert Systems. 2010;1(4):111-22.
44. Isa IS, Saad Z, Omar S, Osman MK, Ahmad KA, Mat Sakim HA. Suitable MLP network activation functions for breast cancer and thyroid disease detection. 2010 Second Int Conf Comput Intell Model Simul. Tuban, Indonesia: IEEE; 2010. p. 39-44. doi: 10.1109/CIMSiM.2010.93. 4
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