Feature Selection based Multivariate Time Series Forecasting: An Application to Antibiotic Resistance Outbreaks Prediction

Abstract Antimicrobial resistance has become one of the most important health problems and global action plans have been proposed globally. Prevention plays a key role in these actions plan and, in this context, we propose the use of Artificial Intelligence, specifically Time Series Forecasting techniques, for predicting future outbreaks of Methicilin-resistant Staphylococcus aereus (MRSA). Infection incidence forecasting is approached as a Feature Selection based Time Series Forecasting problem using multivariate time series composed of incidence of Staphylococcus Aereus Methicilin-sensible and MRSA infections, influenza incidence and total days of therapy of both of Levoflaxin and Oseltamivir antimicrobials. Data were collected from the University Hospital of Getafe (Spain) from January 2009 to January 2018, using months as time granularity. The main contributions of the work are the following: the applications of wrapper feature selection methods where the search strategy is based on multi-objective evolutionary algorithms (MOEA) along with evaluators based on the most powerful state-of-the-art regression algorithms. The performance of the feature selection methods has been measured using the root mean square error (RMSE) and mean absolute error (MAE) performance metrics. A novel multi-criteria decision-making process is proposed in order to select the most satisfactory forecasting model, using the metrics previously mentioned, as well as the slopes of model prediction lines in the 1, 2 and 3 steps-ahead predictions. The multi-criteria decision-making process is applied to the best models resulting from a ranking of databases and regression algorithms obtained through multiple statistical tests. Finally, to the best of our knowledge, this is the first time that a feature selection based multivariate time series methodology is proposed for antibiotic resistance forecasting. Final results show that the best model according to the proposed multi-criteria decision making process provides a RMSE = (0.1349, 0.1304, 0.1325) and a MAE = (0.1003, 0.096, 0.0987) for 1, 2, and 3 steps-ahead predictions.