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Complex Entropy and Its Application in Decision-Making for Medical Diagnosis.

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A novel complex entropy measure is introduced to quantify uncertainty in complex-valued distributions, enhancing decision-making systems. This new method, a generalization of Gini entropy, shows superiority in information fusion and medical diagnosis applications.

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Area of Science:

  • Information Theory
  • Decision Sciences
  • Applied Mathematics

Background:

  • Measuring uncertainty in complex-valued distributions is a significant challenge in decision-making systems.
  • Existing methods struggle with information processing on complex planes.

Purpose of the Study:

  • To propose a new complex entropy measure for quantifying uncertainty in complex-valued distributions.
  • To develop a multisource information fusion algorithm based on the proposed complex entropy for decision-making.
  • To validate the practicability of the developed algorithm in a medical diagnosis context.

Main Methods:

  • Generalization of Gini entropy to create a complex entropy measure.
  • Analysis of the properties of the proposed complex entropy (nonnegativity, boundedness, etc.).
  • Development of a multisource information fusion algorithm utilizing the complex entropy.

Main Results:

  • The proposed complex entropy effectively measures uncertainty in complex-valued distributions.
  • The complex entropy degrades to Gini entropy for standard probability distributions.
  • Numerical examples demonstrate the superiority of the new complex entropy measure.
  • The developed decision-making algorithm shows practical applicability in medical diagnosis.

Conclusions:

  • The novel complex entropy provides a robust method for uncertainty quantification in complex systems.
  • The developed information fusion algorithm enhances decision-making capabilities, particularly in complex-valued data scenarios.
  • The application in medical diagnosis highlights the practical utility and effectiveness of the proposed approach.