Monthly trends, determinants, and forecasting of perinatal mortality in Ghana: a comparison of ARIMA, BPNN, DLNN, and GRNN models
Background Perinatal mortality is a critical indicator of the quality of maternal and newborn care across sub-Saharan Africa. A recent systematic review and meta-analysis estimated Ghana's pooled perinatal mortality rate at 44.8 per 1,000 births, highlighting ongoing barriers to meeting Sustainable Development Goal targets for neonatal survival. Methods We conducted a retrospective, hospital-based time series analysis of 192 monthly observations from January 2010 through December 2025. Perinatal mortality rate (PMR) was defined as the sum of stillbirths and early neonatal deaths per 1,000 births. Stationarity was evaluated using the Augmented Dickey Fuller (ADF) test. Forecasting performance was compared across four models—ARIMA, backpropagation neural network (BPNN), deep learning neural network (DLNN), and generalized regression neural network (GRNN)—with model validation performed on a 2025 temporal holdout. Results The hospital recorded 46,108 live births and 1,152 perinatal deaths, giving an overall PMR of 24.98 per 1,000 births. The undifferenced monthly PMR series was borderline non-stationary (ADF statistic −2.695; p = 0.075), while the first-differenced series was stationary (ADF statistic −7.118; p < 0.001). The best-performing ARIMA model on the 2025 holdout was ARIMA (3, 0, 0). Test-set RMSE values were 11.74 for ARIMA, 14.98 for BPNN, 12.87 for DLNN, and 13.33 for GRNN. In ecological monthly models, higher ANC coverage was associated with lower PMR, whereas higher hypertension burden was associated with higher PMR. Conclusion Perinatal mortality declined over the long term but remained unstable. Among the evaluated models, ARIMA showed the best out-of-sample accuracy, while GRNN was the strongest neural-network comparator. Forecasts should be interpreted as operational projections rather than causal predictions. What is already known on this topic? Perinatal mortality remains high in many low- and middle-income countries, and stillbirths plus early neonatal deaths continue to contribute substantially to under-5 mortality in Ghana ( 1– 6). What this study adds This study provides a 16-year monthly hospital time series, compares classical time-series forecasting with three neural-network approaches, and demonstrates that better ANC coverage and lower hypertension burden track with lower monthly PMR in this setting. How this study might affect research, practice, or policy Monthly PMR surveillance may help maternity hospitals monitor service quality, and ARIMA-based operational forecasting may assist planning for high-risk periods while service-improvement efforts focus on ANC utilization and maternal complication control.