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The increasing burden on healthcare systems due to urbanization, population growth, and the rising prevalence of chronic diseases has necessitated innovative solutions for effective healthcare management. Intelligent healthcare systems, integrating IoT-enabled wearable sensors, fuzzy logic, and neural networks, have emerged as transformative technologies. A literature review highlights advancements in IoT-based health monitoring, green communication techniques, and fuzzy decision-making approaches. The proposed framework utilizes fuzzy logic-based neural networks to enhance prediction accuracy and improve healthcare delivery efficiency. Results demonstrate superior accuracy and reliability compared to traditional frameworks, with a recorded accuracy of 99.2% and minimal error rates. Sensitivity analysis validates the robustness of the proposed methodology, emphasizing its capability for multi-criteria decision-making in intelligent healthcare systems. This research contributes to advancing healthcare technologies and provides insights into developing secure, efficient, and patient-centric healthcare solutions.

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IoT-Based Smart Healthcare Framework Using Fuzzy Decision Making

  • 会议论文

  • 发表日期：

    2026年

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  • Harshini Gadam,
  • Mohan Kumar Meesala,
  • Sravanthi Dontu,
  • Vijayalaxmi Methuku,
  • Tarak Hussain,
  • Tushar Chaudhari

摘要

The increasing burden on healthcare systems due to urbanization, population growth, and the rising prevalence of chronic diseases has necessitated innovative solutions for effective healthcare management. Intelligent healthcare systems, integrating IoT-enabled wearable sensors, fuzzy logic, and neural networks, have emerged as transformative technologies. A literature review highlights advancements in IoT-based health monitoring, green communication techniques, and fuzzy decision-making approaches. The proposed framework utilizes fuzzy logic-based neural networks to enhance prediction accuracy and improve healthcare delivery efficiency. Results demonstrate superior accuracy and reliability compared to traditional frameworks, with a recorded accuracy of 99.2% and minimal error rates. Sensitivity analysis validates the robustness of the proposed methodology, emphasizing its capability for multi-criteria decision-making in intelligent healthcare systems. This research contributes to advancing healthcare technologies and provides insights into developing secure, efficient, and patient-centric healthcare solutions.