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. 2025 Jan 4;25(1):34.
doi: 10.1186/s12889-024-21187-0.

Forecasting cardiovascular disease mortality using artificial neural networks in Sindh, Pakistan

Moiz Qureshi  1   2 , Khushboo Ishaq  3 , Muhammad Daniyal  4 , Hasnain Iftikhar  5   6 , Mohd Ziaur Rehman  7 , S A Atif Salar  8
Affiliations

Forecasting cardiovascular disease mortality using artificial neural networks in Sindh, Pakistan

Moiz Qureshi et al. BMC Public Health. .

Abstract

Cardiovascular disease (CVD) is a leading cause of death and disability worldwide, and its incidence and prevalence are increasing in many countries. Modeling of CVD plays a crucial role in understanding the trend of CVD death cases, evaluating the effectiveness of interventions, and predicting future disease trends. This study aims to investigate the modeling and forecasting of CVD mortality, specifically in the Sindh province of Pakistan. The civil hospital in the Nawabshah area of Sindh province, Pakistan, provided the data set used in this study. It is a time series dataset with actual cardiovascular disease (CVD) mortality cases from 1999 to 2021 included. This study analyzes and forecasts the CVD deaths in the Sindh province of Pakistan using classical time series models, including Naïve, Holt-Winters, and Simple Exponential Smoothing (SES), which have been adopted and compared with a machine learning approach called the Artificial Neural Network Auto-Regressive (ANNAR) model. The performance of both the classical time series models and the ANNAR model has been evaluated using key performance indicators such as Root Mean Square Deviation Error, Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). After comparing the results, it was found that the ANNAR model outperformed all the selected models, demonstrating its effectiveness in predicting CVD mortality and quantifying future disease burden in the Sindh province of Pakistan. The study concludes that the ANNAR model is the best-selected model among the competing models for predicting CVD mortality in the Sindh province. This model provides valuable insights into the impact of interventions aimed at reducing CVD and can assist in formulating health policies and allocating economic resources. By accurately forecasting CVD mortality, policymakers can make informed decisions to address this public health issue effectively.

Keywords: Analyzing and forecasting; Artificial neural network approach; Cardiovascular disease; Mortality; Time series models.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: This study has been approved by the ethical review board of the district office, Sindh with approval no 32E/4/2021. Consent for publication: The authors declare no conflict of interest for this article. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Time series of Yearly death cases of CVD
Fig. 2
Fig. 2
ANNAR model with four inputs one hidden layer with three hidden neurons
Fig. 3
Fig. 3
Residual Diagnostics of CVD death cases for ANNAR, SES, Holt, and Naïve
Fig. 4
Fig. 4
Observed versus fitted graph of CVD using ANNAR and SES
Fig. 5
Fig. 5
QQ norm Plot of Naïve method
Fig. 6
Fig. 6
QQ norm Plot of Holts method
Fig. 7
Fig. 7
QQ norm Plot of SES method
Fig. 8
Fig. 8
QQ norm Plot of ANNAR method
See this image and copyright information in PMC

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