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LSGDM with Biogeography-Based Optimization (BBO) Model for Healthcare Applications.

A Harshavardhan1, Prasanthi Boyapati2, S Neelakandan3

  • 1Department of CSE, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India.

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Summary
This summary is machine-generated.

This study introduces a MapReduce-based large-scale group decision-making (LSDGM) model to enhance healthcare management in Industry 4.0. The novel approach improves decision accuracy using big data analytics and advanced algorithms.

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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Healthcare
  • Big Data Analytics

Background:

  • Healthcare management increasingly relies on effective decision-making, particularly multicriteria group decision-making.
  • Big data analytics offers potential for disease detection and improved healthcare delivery.
  • Limited research exists on large-scale group decision-making (LSDGM) within the context of big data-driven healthcare Industry 4.0.

Purpose of the Study:

  • To develop and present a novel MapReduce-based LSDGM (MR-LSDGM) model tailored for the healthcare Industry 4.0 environment.
  • To enhance the accuracy and efficiency of decision-making processes in healthcare management.
  • To address the gap in research concerning LSDGM in advanced healthcare settings.

Main Methods:

  • The MR-LSDGM model employs a three-stage process: clustering decision-makers (DM), modeling DM preferences, and classification.
  • Decision-makers are clustered using a hybrid approach combining biogeography-based optimization (BBO) and fuzzy C-means (FCM).
  • Subgroup preferences are modeled using the two-tuple fuzzy linguistic representation (2TFLR) technique, with classification utilizing long short-term memory (LSTM) feature extraction and an extreme learning machine (ELM) classifier.

Main Results:

  • Experimental analyses demonstrate the effectiveness of the proposed MR-LSDGM model.
  • The model successfully handles massive datasets through the MapReduce platform.
  • Performance metrics indicate significant improvements in healthcare management decision-making.

Conclusions:

  • The developed MR-LSDGM model offers a robust solution for complex decision-making challenges in healthcare Industry 4.0.
  • The integration of BBO, FCM, 2TFLR, LSTM, and ELM provides a powerful framework for analyzing large-scale group preferences.
  • This research contributes to advancing big data applications in healthcare management and decision support systems.