EPTs-TL: A two-level approach for efficient event prediction in healthcare

Recently, the event prediction on time series (EPTs) was discussed as one of the important and interesting research trends that its usage is growing for taking proper decisions in the various sciences. In the real-world, time series event-based analysis can pose as one of the challenging prediction problems in healthcare, which have a direct impact and a key role in supporting health management. In this paper, an efficient approach of two-level (TL) is proposed to the EPTs problem in healthcare, which named EPTs-TL. At the first level, unseen time series data is predicted by using an enhanced hybrid model based on soft computing technology. Then, a new feature extraction-based method is proposed for fuzzy detection of future events in two-level. The EPTs -TL approach employed concepts of three components: weighting, fuzzy logic, and metaheuristics in two-level of the proposed approach. The empirical results demonstrate the excellent performance of the EPTs -TL approach in comparison to conventional prediction models in healthcare and medicine. Also, the proposed approach can be introduced as a strong tool to handle the complex and uncertain behaviors of time series, analyze unusual variations of those, forewarn the possible critical situations in the society, and fuzzy predict event in healthcare.