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.

最近，时间序列 (EPT) 的事件预测被认为是重要且有趣的研究趋势之一，它的使用正在增长，以便在各种科学中做出正确的决策。在现实世界中，基于时间序列事件的分析可以成为医疗保健中具有挑战性的预测问题之一，它在支持健康管理方面具有直接影响和关键作用。在本文中，针对医疗保健中的EPTs问题提出了一种有效的两级（TL）方法，称为EPTs-TL。第一层，利用基于软计算技术的增强型混合模型，对看不见的时间序列数据进行预测。然后，提出了一种新的基于特征提取的方法，用于两级未来事件的模糊检测。EPTs -TL 方法采用了三个组成部分的概念：加权、模糊逻辑、和元启发式在所提出的方法的两个级别。实证结果表明，与医疗保健和医学领域的传统预测模型相比，EPTs -TL 方法具有出色的性能。此外，所提出的方法可以作为一种强大的工具来处理时间序列的复杂和不确定行为，分析这些行为的异常变化，预警社会中可能的危急情况，以及医疗保健中的模糊预测事件。