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A Novel Method for a COVID-19 Classification of Countries Based on an Intelligent Fuzzy Fractal Approach.

Oscar Castillo1, Patricia Melin1

  • 1Tijuana Institute of Technology, Tijuana 22414, Mexico.

Healthcare (Basel, Switzerland)
|February 13, 2021
PubMed
Summary

A novel hybrid intelligent approach combines fractal dimension and fuzzy logic for classifying countries based on COVID-19 data complexity. This method achieves over 93% accuracy in classifying countries by analyzing time series data.

Keywords:
COVID-19classificationfractal dimensionfuzzy logic

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Area of Science:

  • Complex Systems Analysis
  • Computational Intelligence
  • Epidemiological Data Science

Background:

  • Classifying countries based on epidemiological data presents challenges due to inherent uncertainties and complex dynamics.
  • Traditional methods may struggle to capture the non-linear behavior in time series data of disease spread.

Purpose of the Study:

  • To introduce a hybrid intelligent fuzzy fractal approach for accurate country classification.
  • To leverage fractal dimension and fuzzy logic for analyzing COVID-19 time series data complexity.

Main Methods:

  • Calculating fractal dimensions to quantify the complexity of non-linear dynamic behavior in country-specific time series data.
  • Developing a fuzzy logic system with fuzzy rules using fractal dimensions as inputs for classification.
  • Utilizing confirmed and death case data for COVID-19.

Main Results:

  • The hybrid approach demonstrated high classification accuracy, exceeding 93% in validation across 15 countries.
  • The fuzzy system, trained on 11 countries' data, effectively classified the complexity of COVID-19 time series.

Conclusions:

  • The proposed hybrid intelligent fuzzy fractal approach offers an accurate and effective method for classifying countries based on COVID-19 data complexity.
  • This methodology provides a robust framework for handling uncertainty in epidemiological data analysis.