Key result
No single deep learning model consistently outperforms others across cardiovascular forecasting horizons.
Why the study?
Comparative performance of single and hybrid deep learning models for forecasting multiple cardiovascular health indicators at various time horizons was not established.
No single deep learning architecture performed best across all cardiovascular health indicators and forecasting horizons, highlighting the need for task-specific model selection and careful validation.
No universal model suits all CV forecasts; leaves open task-specific selection pending external validation.
Objective: To compare single and hybrid deep learning models for forecasting six aggregate cardiovascular health indicators at two-, five-, and ten-year horizons. Methods: Convolutional neural networks (CNN), long short-term memory networks (LSTM), gated recurrent units (GRU), and three hybrid architectures were compared for hypertension prevalence, mean systolic blood pressure, diabetes prevalence, mean total cholesterol, and urban and rural mean body mass index. Forecast errors were assessed using mean absolute error, mean absolute percentage error, and root mean squared error. The manuscript also describes a separate patient-level heart disease classification workflow using two public datasets, recursive feature elimination, classical machine-learning models, and a stacking ensemble with a support vector machine meta-learner. Results: The best-performing forecast model varied by indicator and horizon. CNN–GRU had the lowest reported error for two-year hypertension forecasts, while GRU led at five and ten years. CNN–LSTM led the two- and five-year cholesterol forecasts, and LSTM–GRU led at ten years. Model rankings also changed across blood pressure, diabetes prevalence, and urban and rural BMI forecasts. The supplied numerical tables report aggregate forecasts but do not include classification performance results for the proposed stacking ensemble. Conclusion: No single architecture performed best across all indicators and horizons. The findings support selecting models for specific forecasting tasks, followed by careful validation. Because the temporal validation design, study geography, observation years, and forecast uncertainty are not reported, the results do not establish clinical benefit.
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Sharif et al. (2026) studied Cardiovascular health indicators. Hybrid deep learning models (CNN-LSTM, CNN-GRU, LSTM-GRU) vs. Single deep learning models (CNN, LSTM, GRU) was evaluated on Forecast errors (MAE, MAPE, RMSE). No single deep learning architecture performed best across all cardiovascular health indicators and forecasting horizons, with model rankings varying by target and lead time.
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