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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
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Multi-criterion Intelligent Decision Support system for COVID-19.

Lakshita Aggarwal1, Puneet Goswami1, Shelly Sachdeva2

  • 1Department of Computer Science & Engineering, SRM University Delhi-NCR Sonepat, Haryana, India.

Applied Soft Computing
|January 4, 2021
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Summary
This summary is machine-generated.

This study proposes a machine learning-based Decision Support System (DSS) to improve COVID-19 risk prediction. The novel Additive Utility Assumption Approach enhances multi-criterion decision-making for pandemic management.

Keywords:
Covid-19EpidemiologyLearning methodMachine learningMulti-criterion Intelligent Decision Support system

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Area of Science:

  • * Computational epidemiology
  • * Machine learning applications in public health

Background:

  • * Digital contact tracing apps showed limitations during community spread phases of COVID-19.
  • * Existing early warning systems require enhancement for effective national and global risk management.
  • * Machine learning offers potential for developing advanced Decision Support Systems (DSS).

Purpose of the Study:

  • * To propose an Additive Utility Assumption Approach for criterion comparison in a Multi-criterion Intelligent Decision Support System (MCDSS) for COVID-19.
  • * To accurately predict COVID-19 risk factors using well-defined input parameters.
  • * To validate the proposed approach using the Susceptible, Exposed, Infected, and Recovered (SEIR) model.

Main Methods:

  • * Development of an Additive Utility Assumption Approach for MCDSS.
  • * Empirical validation using the standard SEIR model.
  • * Statistical analysis of COVID-19 data from top Indian states using Weka and IBM Cognos software.

Main Results:

  • * Comparative analysis demonstrated the proposed approach's potential against existing methods.
  • * Performance metrics including Precision, Recall, F-Score, Matthews Correlation Coefficient (MCC), Receiver Operating Characteristics (ROC), and Precision-Recall Curve (PRC) were used for validation.
  • * Accurate prediction of pandemic outbreak situations was achieved.

Conclusions:

  • * The proposed Additive Utility Assumption Approach offers a robust MCDSS for COVID-19 risk assessment.
  • * The approach provides valuable insights for improving pandemic preparedness and response strategies.
  • * This research can stimulate further investigation into AI-driven solutions for public health crises.