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Fuzzy Logic System Implementation on the Performance Parameters of Health Data Management Frameworks
Sonali Vyas1, Shaurya Gupta1, Deepshikha Bhargava2
1University of Petroleum and Energy Studies, Dehradun, India.
Journal of Healthcare Engineering
|April 22, 2022
Summary
Fuzzy logic systems effectively manage unstructured health data from wireless sensors, improving accuracy and energy efficiency in healthcare applications. This approach addresses data uncertainties and noise for better diagnosis and decision-making.
Area of Science:
- Health Informatics
- Artificial Intelligence
- Sensor Technology
Background:
- Wireless sensors and wearable devices generate vast amounts of unstructured and heterogeneous health data.
- Existing frameworks focus on data exchange and security, with limited attention to data structuring and interpretation.
- Sensor performance is impacted by energy constraints, introducing uncertainties, noise, and errors into health data.
Purpose of the Study:
- To review the integration of fuzzy logic systems and algorithms in healthcare applications.
- To highlight the effectiveness of fuzzy logic in handling raw medical data uncertainties and managing data.
- To assess the enhancement of healthcare frameworks in terms of accuracy, precision, and data handling capabilities.
Main Methods:
- Review of existing literature integrating fuzzy logic systems with healthcare applications.
- Analysis of how fuzzy logic addresses uncertainties, noise, and data management challenges in wireless health data.
- Evaluation of artificial intelligence, neural network, and optimization techniques in fuzzy logic integration.
Main Results:
- Fuzzy logic systems demonstrate effectiveness and energy efficiency in managing uncertainties and noise in raw medical data.
- Integration of fuzzy logic enhances the accuracy, precision, and data handling capabilities of healthcare applications and frameworks.
- Fuzzy logic aids in the classification, noise removal, and interoperation of heterogeneous health data for improved decision-making.
Conclusions:
- Fuzzy logic systems offer a promising approach for structuring and interpreting complex health data from wireless sensors.
- Further research should explore expanding fuzzy logic adaptability within cloud architectures and incorporating diverse machine learning methodologies.
- The energy efficiency and accuracy improvements underscore the value of fuzzy logic in advancing wireless healthcare services.
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