Forecasting the incidence frequencies of schizophrenia using deep learning

Abstract

Mental disorders are becoming increasingly prevalent worldwide, and accurate incidence forecasting is crucial for effective mental health strategies. This study developed a long short-term memory (LSTM)-based recurrent neural network model to predict schizophrenia in inpatients in Taiwan. Data was collected on individuals aged over 20 years and diagnosed with schizophrenia between 1998 and 2015 from the National Health Insurance Research Database (NHIRD). The study compared six models, including LSTM, exponential smoothing, autoregressive integrated moving average, particle swarm optimization (PSO), PSO-based support vector regression, and deep neural network models, in terms of their predictive performance. The results showed that the LSTM model had the best accuracy, with the lowest mean absolute percentage error (2.34), root mean square error (157.42), and mean average error (154,831.70). This finding highlights the reliability of the LSTM model for forecasting mental disorder incidence. The study's findings provide valuable insights that can help government administrators devise clinical strategies for schizophrenia, and policymakers can use these predictions to formulate healthcare education and financial planning initiatives, fostering support networks for patients, caregivers, and the public.