Comparison of Artificial Neural Network and Box- Jenkins Models to Predict the Number of Patients with Hypertension in Kalar

Layla A Ahmed

doi:10.30526/33.4.2516

Abstract

Artificial Neural Network (ANN) is widely used in many complex applications. Artificial neural network is a statistical intelligent technique resembling the characteristic of the human neural network. The prediction of time series from the important topics in statistical sciences to assist administrations in the planning and make the accurate decisions, so the aim of this study is to analysis the monthly hypertension in Kalar for the period (January 2011- June 2018) by applying an autoregressive –integrated- moving average model and artificial neural networks and choose the best and most efficient model for patients with hypertension in Kalar through the comparison between neural networks and Box- Jenkins models on a data set for predict. Comparisons between the models has been performed using Criterion indicator Akaike information Criterion, mean square of error, root mean square of error, and mean absolute percentage error, concluding that the prediction for patients with hypertension by using artificial neural networks model is the best.

Highlights

Last years, after increasing the number of patients with chronic hypertension disease, it was to be highlighted to study this disease and the use of statistical methods and artificial intelligence techniques
Results of Box- Jenkins Figure 3, represents the series monthly hypertension and we show that data are stationary in the variance, but not stationary in the mean when we plot autocorrelation functions and partial autocorrelation function for the data
We treat the outlier problem, take the first difference for the data and plot Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) again for the difference time series; we show that the series become stationary in the mean and variance

Summary

Introduction

After increasing the number of patients with chronic hypertension disease, it was to be highlighted to study this disease and the use of statistical methods and artificial intelligence techniques. Hypertension is defined as the abnormal high blood pressure Uncontrolled high blood pressure makes you more likely to get heart disease, stroke, and kidney disease [2]. The time series forecasting assumes that the future values a linear combination of historical data. There are various time series forecasting models; the most highly frequently approach to fit such model is Box and Jenkins for fitting ARIMA model. Box and Jenkins (1970) generalized the ARIM model to deal with seasonality [4]. Tiao and Box (1979) described a practical to ARMA modeling of multivariate time series data by three stages: identification, estimation of the parameters and model checking [5]

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Results

Conclusion