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Deep learning architectures for modeling and forecasting stroke cases in Ghana.

Abdul-Karim Iddrisu1, Morongwa Gabanakgosi1, Abubakar Hudu Siddick2

  • 1Department of Statistics, University of Botswana, Gaborone, Botswana.

Journal of Stroke and Cerebrovascular Diseases : the Official Journal of National Stroke Association
|January 23, 2026
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Summary

Ghana faces a high stroke burden, with deep learning models forecasting a stabilizing trend. The Long Short-Term Memory (LSTM) model proved most effective for predicting future stroke incidence.

Keywords:
Bayesian Long Short-Term Memory (BLSTM)Bayesian convolution LSTM(BConv)deep learningstroke

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Area of Science:

  • Public Health
  • Epidemiology
  • Artificial Intelligence in Healthcare

Background:

  • Stroke is a leading cause of death and disability globally, with a severe impact in Ghana.
  • Limited local data and predictive models hinder effective stroke intervention planning in Ghana.
  • This study addresses the need for data-driven public health strategies by modeling stroke incidence.

Purpose of the Study:

  • To model and forecast stroke incidence in Ghana using advanced deep learning techniques.
  • To identify the most effective deep learning model for stroke incidence forecasting.
  • To inform public health strategies for stroke prevention and management.

Main Methods:

  • Utilized monthly stroke case data from Ghana Health Service (2018-2023).
  • Employed Long Short-Term Memory (LSTM), Bayesian LSTM (BLSTM), Convolutional LSTM (ConvLSTM), and Bayesian ConvLSTM (BConvLSTM) models.
  • Included diabetes prevalence as a covariate and evaluated models using MAE, MSE, RMSE, and MAPE.

Main Results:

  • LSTM and BLSTM models demonstrated strong forecasting performance, with LSTM showing the lowest errors.
  • ConvLSTM and BConvLSTM models significantly underperformed.
  • Forecasts indicate initial variability in 2024 (1,694-2,007 cases/month) followed by stabilization by 2028 (1,774-1,781 cases/month).

Conclusions:

  • Ghana faces a persistently high but stabilizing stroke burden.
  • Highlights the critical need for interventions targeting modifiable risk factors, especially diabetes.
  • Recommends LSTM as the optimal model for forecasting stroke incidence in Ghana.