Application of a novel approach for dementia prevalence prediction in Taiwan.

Amid the rapidly aging global population, dementia cases are rising at an alarming rate. Dementia has become a major public health challenge, exerting profound impacts on socioeconomic systems and overall human well-being. The condition progressively deteriorates cognitive abilities such as memory, judgment, comprehension, and language, eventually resulting in the loss of independent daily functioning. In addition, patients often experience neuropsychiatric symptoms that severely diminish their quality of life. This study proposes an optimized machine learning model the Flying Geese Optimization Algorithm Support Vector Regression (FGOASVR) to effectively predict trends in dementia prevalence. The empirical analysis utilizes annual dementia diagnostic data from 1998 to 2023, obtained from Taiwan's National Health Insurance Research Database (NHIRD). To validate model performance, FGOASVR was compared against three categories of forecasting models: Statistical models: Autoregressive Integrated Moving Average (ARIMA) and Holt-Winters Exponential Smoothing (HWETS); Deep learning model: Long Short-Term Memory (LSTM); Hybrid models: Support Vector Regression (SVR), Particle Swarm Optimization SVR (PSOSVR), Differential Evolution SVR (DESVR), Whale Optimization Algorithm SVR (WOASVR), and Harris Hawk Optimization SVR (HHOSVR). Performance was assessed using standard forecasting metrics Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The FGOASVR model achieved the highest accuracy, with average MAPE values of 3.17 and 3.42, and RMSE values of 0.69 and 0.96 for males and females, respectively. These results confirm that FGOASVR delivers superior precision and stability in forecasting dementia trends in Taiwan, demonstrating its strong potential for advancing data-driven public health prediction and policy development.