Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
BMC Medical Research Methodology
Published in: BMC Medical Research Methodology 1/2020

Open Access 01-12-2020 | Artificial Intelligence | Research article

Time series prediction of under-five mortality rates for Nigeria: comparative analysis of artificial neural networks, Holt-Winters exponential smoothing and autoregressive integrated moving average models

Authors: Daniel Adedayo Adeyinka, Nazeem Muhajarine

Published in: BMC Medical Research Methodology | Issue 1/2020

Login to get access

Abstract

Background

Accurate forecasting model for under-five mortality rate (U5MR) is essential for policy actions and planning. While studies have used traditional time series modeling techniques (e.g., autoregressive integrated moving average (ARIMA) and Holt-Winters smoothing exponential methods), their appropriateness to predict noisy and non-linear data (such as childhood mortality) has been debated. The objective of this study was to model long-term U5MR with group method of data handling (GMDH)-type artificial neural network (ANN), and compare the forecasts with the commonly used conventional statistical methods—ARIMA regression and Holt-Winters exponential smoothing models.

Methods

The historical dataset of annual U5MR in Nigeria from 1964 to 2017 was obtained from the official website of World Bank. The optimal models for each forecasting methods were used for forecasting mortality rates to 2030 (ending of Sustainable Development Goal era). The predictive performances of the three methods were evaluated, based on root mean squared errors (RMSE), root mean absolute error (RMAE) and modified Nash-Sutcliffe efficiency (NSE) coefficient. Statistically significant differences in loss function between forecasts of GMDH-type ANN model compared to each of the ARIMA and Holt-Winters models were assessed with Diebold-Mariano (DM) test and Deming regression.

Results

The modified NSE coefficient was slightly lower for Holt-Winters methods (96.7%), compared to GMDH-type ANN (99.8%) and ARIMA (99.6%). The RMSE of GMDH-type ANN (0.09) was lower than ARIMA (0.23) and Holt-Winters (2.87). Similarly, RMAE was lowest for GMDH-type ANN (0.25), compared with ARIMA (0.41) and Holt-Winters (1.20). From the DM test, the mean absolute error (MAE) was significantly lower for GMDH-type ANN, compared with ARIMA (difference = 0.11, p-value = 0.0003), and Holt-Winters model (difference = 0.62, p-value< 0.001). Based on the intercepts from Deming regression, the predictions from GMDH-type ANN were more accurate (β0 = 0.004 ± standard error: 0.06; 95% confidence interval: − 0.113 to 0.122).

Conclusions

GMDH-type neural network performed better in predicting and forecasting of under-five mortality rates for Nigeria, compared to the ARIMA and Holt-Winters models. Therefore, GMDH-type ANN might be more suitable for data with non-linear or unknown distribution, such as childhood mortality. GMDH-type ANN increases forecasting accuracy of childhood mortalities in order to inform policy actions in Nigeria.
Appendix
Available only for authorised users
Literature
1.
2.
3.
Zhang G, Eddy Patuwo BY, Hu M. Forecasting with artificial neural networks: The state of the art. Int J Forecast. 1998;14(1):35–62.CrossRef
4.
Zhang GP. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing. 2003;50:159–75.CrossRef
5.
Al-Maqaleh BM, Al-Mansoub AA, Al-Badani FN. Forecasting using artificial neural network and statistics models. Educ Manag Eng. 2016;3:20–32.
6.
Kihoro J, Otieno R, Wafula C. Seasonal time series forecasting: a comparative study of Arima and ann models. African J Sci Technol. 2006;5(2):41–9.CrossRef
7.
Aladag CH. A new architecture selection method based on tabu search for artificial neural networks. Expert Syst Appl. 2011;38(4):3287–93.CrossRef
8.
Shi L, Wang XC, Wang YS, Shi L, Wang XC, Wang YS. Artificial neural network models for predicting 1-year mortality in elderly patients with intertrochanteric fractures in China. Brazilian J Med Biol Res. 2013;46(11):993–9.CrossRef
9.
Hsieh MH, Hsieh MJ, Chen CM, Hsieh CC, Chao CM, Lai CC. Comparison of machine learning models for the prediction of mortality of patients with unplanned extubation in intensive care units. Sci Rep. 2018;8(1):1–7.CrossRef
10.
Sakr S, Elshawi R, Ahmed AM, Qureshi WT, Brawner CA, Keteyian SJ, et al. Comparison of machine learning techniques to predict all-cause mortality using fitness data: the Henry Ford exercise testing (FIT) project. BMC Med Inform Decis Mak. 2017;17(1):174.PubMedPubMedCentralCrossRef
11.
Son YJ, Kim HG, Kim EH, Choi S, Lee SK. Application of support vector machine for prediction of medication adherence in heart failure patients. Healthc Inform Res. 2010;16(4):253–9.PubMedPubMedCentralCrossRef
12.
Naim I, Mahara T. Comparative analysis of Univariate forecasting techniques for industrial natural gas consumption. Image, Graph Signal Process. 2018;5:33–44.CrossRef
13.
Vochozka M. Practical comparison of results of statistic regression analysis and neural network regression analysis. Littera Scr. 2016;9(2):156–68.
14.
Verplancke T, Van Looy S, Benoit D, Vansteelandt S, Depuydt P, De Turck F, et al. Support vector machine versus logistic regression modeling for prediction of hospital mortality in critically ill patients with haematological malignancies. BMC Med Inform Decis Mak. 2008;8(1):56.PubMedPubMedCentralCrossRef
15.
Parsaeian M, Mohammad K, Mahmoudi M, Zeraati H. Comparison of logistic regression and artificial neural network in low Back pain prediction: second National Health Survey. Iran J Public Health. 2012;41(6):86–92.PubMedPubMedCentral
16.
17.
Blagojević M, Papić M, Vujičić M, Šućurović M. Artificial Neural Network Model for Predicting Air Pollution. Case Study of the Moravica District, Serbia. Environ Prot Eng. 2018;44(1):129–39.
18.
Oustimov A, Vu V. Artificial neural networks in the Cancer genomics frontier. Transl Cancer Res. 2014;3(3):191–201.
19.
Raut RD, Dudul S V. Arrhythmias classification with MLP neural network and statistical analysis. In: Proceedings - 1st International Conference on Emerging Trends in Engineering and Technology. 2008. p. 553–558.
20.
Abdel-Aal RE. GMDH-based feature ranking and selection for improved classification of medical data. J Biomed Inform. 2005;38(6):456–68.PubMedCrossRef
21.
Kondo T, Pandya A, Zurada JM. GMDH-type neural networks and their application to the medical image recognition of the lungs, Proceedings of the SICE Annual Conference International Session Papers (IEEE Cat No99TH8456), Morioka, Japan; 1999. p. 1181–6.
22.
Usher D, Dumskyj M, Himaga M, Williamson TH, Nussey S, Boyce J. Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening. Diabet Med. 2004;21(1):84–90.PubMedCrossRef
23.
Ghazanfari N, Gholami S, Emad A, Shekarchi M. Evaluation of GMDH and MLP networks for prediction of compressive strength and workability of concrete. Bull la Société R des Sci Liège. 2017;86:855–68.
24.
Do QH, Yen TTH. Predicting primary commodity prices in the international market: an application of group method of data handling neural network. J Manag Inf Decis Sci. 2019;4:471–82.
25.
Lopes MNG, Da Rocha BRP, Vieira AC, De Sá JAS, Rolim PAM, Da Silva AG. Artificial neural networks approaches for predicting the potential for hydropower generation: a case study for Amazon region. J Intell Fuzzy Syst. 2019;36(6):5757–72.CrossRef
26.
United Nations Data Revolution Group. A world that counts: Mobilizing the data revolution for sustainable development. 2014. Available from: www.​undatarevolution​.​org. [cited 2020 Feb 25].
27.
Farlow SJ. The GMDH algorithm of Ivakhnenko. Am Stat. 1981;35(4):210–5.
28.
Onwubolu G. GMDH-Methodology and Implementation in MATLAB GMDH-methodology and implementation in MATLAB; 2016. p. 284.CrossRef
29.
30.
31.
32.
Box GEP, Tiao GC. Intervention analysis with applications to economic and environmental problems. J Am Stat Assoc. 1975;70(349):70–9.CrossRef
33.
Pradeepkumar D, Ravi V. Forecasting financial time series volatility using particle swarm optimization trained Quantile regression neural network. Appl Soft Comput J. 2017;58:35–52.CrossRef
34.
Kim WJ, Jung G, Choi SY. Forecasting CDS term structure based on Nelson-Siegel model and machine learning. Complex Econ Bus. 2020;2020:1–23.
35.
Stefenon SF, Dal Molin Ribeiro MH, Nied A, Mariani VC, Coelho dos LS, Menegat da Rocha DF, et al. Wavelet group method of data handling for fault prediction in electrical power insulators. Int J Electr Power Energy Syst. 2020;123:106269.CrossRef
36.
37.
Kuha J. AIC and BIC: comparisons of assumptions and performance. Sociol Methods Res. 2004;33(2):188–229.CrossRef
38.
Chatfield C. The Holt-winters forecasting procedure. Appl Stat. 1978;27(3):264–79.CrossRef
39.
40.
41.
Macek K. Pareto principle in Datamining: an above-average fencing algorithm. Acta Polytech. 2008;48(6):55–9.
42.
Allen DE, Hooper VJ. Generalized correlation measures of causality and forecasts of the VIX using non-linear models. Sustainability. 2018;10(2695):132–46.
43.
44.
Berry MJ, Linoff GS. Data Mining Techniques. New York: Wiley; 1997.
45.
Xu S, Cheng L. A Novel Approach for Determining the Optimal Number of Hidden Layer Neurons for FNN’s and Its Application in Data Mining, 5th International Conference on Information Technology and Applications; 2008. p. 683–6.
46.
Banica L, Pirvu D, Hagiu A. Intelligent financial forecasting, the key for a successful management. Int J Acad Res Accounting, Financ Manag Sci. 2012;2(3):192–206.
47.
Armstrong JS, Collopy F. Error measures for generalizing about forecasting methods: empirical comparisons. Int J Forecast. 1992;8(1):69–80.CrossRef
48.
Makridakis S. Accuracy measures: theoretical and practical concerns. Int J Forecast. 1993;9(4):527–9.CrossRef
49.
Vandeput N. Data science for supply chain forecast; 2018. p. 223.
50.
Phogat V, Ma S, Jw C, Simunek J. Statistical assessment of a numerical model simulating agro hydro-chemical processes in soil under drip Fertigated mandarin tree. Irrig Drain Sys Eng. 2016;5(1):1–9.
51.
Tapak L, Rahmani A, Moghimbeigi A. Prediction the groundwater level of Hamadan-Bahar plain, west of Iran using support vector machines. J Res Health Sci. 2014;14(1):81–6.PubMed
52.
Diebold FX, Mariano RS. Comparing predictive accuracy. J Bus Econ Stat. 1995;13(3):253–63.
53.
Pavlicek J, Kristoufek L. Nowcasting unemployment rates with google searches: Evidence from the Visegrad Group countries. PLoS One. 2015;10(5):e0127084.PubMedPubMedCentralCrossRef
54.
Pai P-F, Lin C-S. A hybrid ARIMA and support vector machines model in stock price forecasting. Omega. 2005;33:497–505.CrossRef
55.
Martin RF. General Deming regression for estimating systematic Bias and its confidence interval in method-comparison studies. Clin Chem. 2000;46(1):100–4.PubMedCrossRef
56.
Francq BG, Govaerts BB. Measurement methods comparison with errors-in-variables regressions. From horizontal to vertical OLS regression, review and new perspectives. Chemom Intell Lab Syst. 2014;134(15):123–39.CrossRef
57.
Sárbu C, Liteanu V, Bâldea M. Evaluation and validation of analytical methods by regression analysis in: reviews in analytical chemistry. Rev Anal Chem. 2000;19(6):467–88.CrossRef
58.
Koutsoyiannis D, Yao H, Georgakakos A. Medium-range flow prediction for the Nile: a comparison of stochastic and deterministic methods. Hydrol Sci J. 2008;53(1):142–64.CrossRef
59.
Koutsoyiannis D. Uncertainty, entropy, scaling and hydrological stochastics. Hydrol Sci J. 2005;50(3):381–404.
60.
Purwanto D, Eswaran C, Logeswaran R. A Comparison of ARIMA, neural network and linear regression models for the prediction of infant mortality rate, 2010 Fourth Asia International Conference on Mathematical/Analytical Modelling and Computer Simulation; 2010. p. 34–9.
61.
Zernikow B, Holtmannspoetter K, Michel E, Pielemeier W, Hornschuh F, Westermann A, et al. Artificial neural network for risk assessment in preterm neonates. Arch Dis Child Fetal Neonatal Ed. 1998;79(2):F129–34.PubMedPubMedCentralCrossRef
62.
63.
64.
Metadata
Title
Time series prediction of under-five mortality rates for Nigeria: comparative analysis of artificial neural networks, Holt-Winters exponential smoothing and autoregressive integrated moving average models
Authors
Daniel Adedayo Adeyinka
Nazeem Muhajarine
Publication date
01-12-2020
Publisher
BioMed Central
Published in
BMC Medical Research Methodology / Issue 1/2020
Electronic ISSN: 1471-2288
DOI
https://doi.org/10.1186/s12874-020-01159-9

Other articles of this Issue 1/2020

BMC Medical Research Methodology 1/2020 Go to the issue

Research article

A computational method to quantitatively measure pediatric drug safety using electronic medical records

Research article

Developing strategies to improve fidelity of delivery of, and engagement with, a complex intervention to improve independence in dementia: a mixed methods study

Software

schema: an open-source, distributed mobile platform for deploying mHealth research tools and interventions

Research article

Combining the theory of change and realist evaluation approaches to elicit an initial program theory of the MomConnect program in South Africa

Research article

Survival prediction models since liver transplantation - comparisons between Cox models and machine learning techniques

Commentary

A tutorial on methodological studies: the what, when, how and why