Comparative Analysis of Predictive Models for Under-Five Mortality Rates in Ghana: Integrating Artificial Neural Networks, Bayesian Structural Time Series, and Seasonal Autoregressive Integrated Moving Average

Introduction Under-five mortality remains a critical public health concern in Ghana, with numerous efforts aimed at understanding its drivers and predicting future trends. This study aims to perform a comparative analysis of predictive models for under-five mortality rates, integrating Artificial Neural Networks (ANN), Bayesian Structural Time Series (BSTS), and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. Methods This study used a dataset of Ghana’s under-five mortality rate from 1964 to 2017, obtained from the World Bank. Data was analyzed using R software. Missing values were imputed, outliers were treated, and stationarity ensured through transformations. The SARIMA model was fitted using ACF/PACF analysis and seasonal parameters, while ANN was trained with optimized hyperparameters on a train-test split. The BSTS model incorporated trend and seasonal components, estimated via Bayesian inference. Model performance was compared using metrics like MSE, MAE, and R², with forecasting accuracy evaluated across all models. Results The median value was 127.4, and the under-5 mortality rate in Ghana is higher than the median for all 241 countries in the exploratory analysis. The mean value of 132.2 and the standard deviation of 54.96 indicated a significant amount of variability in the data. The SARIMA model, despite being a good fit with no significant autocorrelation in residuals, is outperformed by the BSTS model. ANN Model Performance: The ANN model performs better than SARIMA but is not as effective as BSTS. The forecast values of BSTS were chosen for predicting under-5 mortality. Conclusion Comparing advanced models like BSTS with ANN and SARIMA, we can better predict under-five mortality rates in Ghana. BSTS was the best model for the prediction of under 5 mortality. Findings on reliable predicted values help improve child health outcomes and inform policies. Further studies can be conducted combining modern machine learning techniques with statistical methods to obtain more robost findings.