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Tuberculosis incidence predicting system using time series neural network in Iran

نویسندگان

  • Ahmad Ali Hanafi-Bojd Department of Medical Entomology, Tehran University of Medical Sciences, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
  • Atefeh Sedighnia Department of Medical Informatics, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran; Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.
  • Mahshid Nasehi Department of Epidemiology, School of Public Health, Iran University of Medical Sciences, Infectious Diseases Management Center of Ministry of Health and Medical Education, Tehran, Iran.
  • Sharareh Rostam Niakan Kalhori Department of Medical Informatics, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran; Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.

چکیده

Background: Tuberculosis (TB) is an important infectious disease with high mortality in the world. None of the countries stay safe from TB. Nowadays, different factors such as Co-morbidities, increase TB incidence. World Health Organization (WHO) last report about Iran's TB status shows rising trend of multidrug-resistant tuberculosis (MDR-TB) and HIV/TB. More than 95% illness and death of TB cases are in developing countries. The most infections are in South East Asia and West Pacific that 56% of them are new cases in the world. The incidence is actually new cases of each year. Incidence prediction is affecting TB prevention, management and control. The purpose of this study is designing and creating a system to predict TB incidence by time series artificial neural networks (ANN) in Iran. Methods: This study is a retrospective analytic. 10651 TB cases that registered on Iran’s Stop TB System from March 2014 to March 2016, Were analyzed. Most of reliable data used directly, some of them merged together and create new indicators and two columns used to compute a new indicator. At first, effective variables were evaluating with correlation coefficient tests then extracting by linear regression on SPSS statistical software, version 20 (IBM, Armonk, NY, USA). We used different algorithms and number of neurons in hidden layer and delay in time series neural network. R, MSE (mean squared error) and regression graph were used for compare and select the best network. Incidence prediction neural network were designed on MATLAB® software, version R2014a (Mathworks Inc., Natick, MA, USA). Results: At first, 23 independent variables entered to study. After correlation coefficient and regression, 12 variables with P≤0.01 in Spearman and P≤0.05 in Pearson were selected. We had the best value of R, MSE (mean squared error) and also regression graph in train, validation and tested by Bayesian regularization algorithm with 10 neuron in hidden layer and two delay. Conclusion: This study showed that artificial neural network had acceptable function to extract knowledge from TB raw data; ANN is beneficial to TB incidence prediction.

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