University of Petroleum and Energy Studies, Dehradun, India.
School of Computing, DIT University, Dehradun, India.
J Healthc Eng. 2022 Apr 12;2022:9382322. doi: 10.1155/2022/9382322. eCollection 2022.
The development of wireless sensors and wearable devices has led health care services to the new paramount. The extensive use of sensors, nodes, and devices in health care services generate an enormous amount of health data which is generally unstructured and heterogeneous. Many generous methods and frameworks have been developed for efficient data exchange frameworks, security protocols for data security and privacy. However, very less emphasis has been devoted to structuring and interpreting health data by fuzzy logic systems. The wireless sensors and device performances are affected by the remaining battery/energy, which induces uncertainties, noise, and errors. The classification, noise removal, and accurate interoperation of health data are critical for taking accurate diagnosis and decision making. Fuzzy logic system and algorithms were found to be effective and energy efficient in handling the challenges of raw medical data uncertainties and data management. The integration of fuzzy logic is based on artificial intelligence, neural network, and optimization techniques. The present work entails the review of various works which integrate fuzzy logic systems and algorithms for enhancing the performance of healthcare-related apps and framework in terms of accuracy, precision, training, and testing data capabilities. Future research should concentrate on expanding the adaptability of the reasoning component by incorporating other features into the present cloud architecture and experimenting with various machine learning methodologies.
无线传感器和可穿戴设备的发展将医疗保健服务提升到了新的高度。传感器、节点和设备在医疗保健服务中的广泛应用产生了大量的健康数据，这些数据通常是非结构化和异构的。已经开发了许多方法和框架来实现高效的数据交换框架、数据安全和隐私的安全协议。然而，很少有人关注模糊逻辑系统对健康数据的结构和解释。无线传感器和设备的性能受到剩余电池/能量的影响，这会导致不确定性、噪声和误差。健康数据的分类、噪声消除和准确操作对于进行准确的诊断和决策至关重要。模糊逻辑系统和算法在处理原始医疗数据不确定性和数据管理方面被证明是有效和节能的。模糊逻辑的集成基于人工智能、神经网络和优化技术。目前的工作涉及对各种将模糊逻辑系统和算法集成到医疗保健相关应用程序和框架中的工作进行审查，以提高其在准确性、精度、训练和测试数据能力方面的性能。未来的研究应该集中在通过将其他功能纳入当前的云架构并尝试各种机器学习方法来扩展推理组件的适应性。
