Castillo Oscar, Melin Patricia
Tijuana Institute of Technology, Tijuana 22414, Mexico.
Healthcare (Basel). 2021 Feb 10;9(2):196. doi: 10.3390/healthcare9020196.
We outline in this article a hybrid intelligent fuzzy fractal approach for classification of countries based on a mixture of fractal theoretical concepts and fuzzy logic mathematical constructs. The mathematical definition of the fractal dimension provides a way to estimate the complexity of the non-linear dynamic behavior exhibited by the time series of the countries. Fuzzy logic offers a way to represent and handle the inherent uncertainty of the classification problem. The hybrid intelligent approach is composed of a fuzzy system formed by a set of fuzzy rules that uses the fractal dimensions of the data as inputs and produce as a final output the classification of countries. The hybrid approach calculations are based on the COVID-19 data of confirmed and death cases. The main contribution is the proposed hybrid approach composed of the fractal dimension definition and fuzzy logic concepts for achieving an accurate classification of countries based on the complexity of the COVID-19 time series data. Publicly available datasets of 11 countries have been the basis to construct the fuzzy system and 15 different countries were considered in the validation of the proposed classification approach. Simulation results show that a classification accuracy over 93% can be achieved, which can be considered good for this complex problem.
在本文中，我们概述了一种混合智能模糊分形方法，用于基于分形理论概念和模糊逻辑数学结构的混合对国家进行分类。分形维数的数学定义提供了一种方法来估计国家时间序列所表现出的非线性动态行为的复杂性。模糊逻辑提供了一种表示和处理分类问题固有不确定性的方法。这种混合智能方法由一个模糊系统组成，该系统由一组模糊规则构成，这些规则将数据的分形维数用作输入，并最终输出国家的分类结果。混合方法的计算基于新冠肺炎确诊病例和死亡病例的数据。主要贡献在于所提出的由分形维数定义和模糊逻辑概念组成的混合方法，用于基于新冠肺炎时间序列数据的复杂性对国家进行准确分类。11个国家的公开可用数据集是构建模糊系统的基础，在所提出的分类方法的验证中考虑了15个不同的国家。仿真结果表明，可以实现超过93%的分类准确率，对于这个复杂问题而言，这可以被认为是不错的。
