Machine-learning-based analysis of biomedical time-series data: the monitoring and prediction of disease progression

Abstract

Abstract This study examines the spatial and temporal patterns of influenza and malaria incidence using an ARMA-BP combination model. The approach employs the dynamic series method to identify epidemic patterns of these diseases while assessing serial autocorrelation coefficients, performing randomness tests, and establishing a forecasting model. Additionally, it evaluates the impact of seasonal and meteorological factors on the epidemiology of influenza and malaria to ascertain the model’s efficacy in predicting incidence rates and trends. The findings indicate that the peak period for influenza incidence typically occurs during the transition from winter to spring, specifically between weeks 2 and 14. The correlation coefficients between temperature variables and malaria incidence generally ranged from 0.7 to 0.9. The ARMA-BP model demonstrated robust short-term predictive capabilities for influenza, showing a high degree of concordance in predictions for 2021 and 2022, though it performed less satisfactorily for 2023. For malaria, the predicted and actual incidence trends were largely consistent, with prediction errors consistently below 0.01. Consequently, this underscores the need for enhanced data collection on factors influencing disease dynamics. This research provides valuable decision-making support, scientific insights, and theoretical guidance for enhancing disease monitoring and prediction strategies.