IEEE J Biomed Health Inform. 2018 Sep;22(5):1605-1618. doi: 10.1109/JBHI.2018.2799198. Epub 2018 Jan 30.
With advancements in information and communication technology, there is a steep increase in the remote healthcare applications in which patients can get treatment from the remote places also. The data collected about the patients by remote healthcare applications constitute big data because it varies with volume, velocity, variety, veracity, and value. To process such a large collection of heterogeneous data is one of the biggest challenges which requires a specialized approach. To address this challenge, a new fuzzy rule based classifier is presented in this paper with an aim to provide Healthcare-as-a-Service. The proposed scheme is based upon the initial cluster formation, retrieval, and processing of the big data in cloud environment. Then, a fuzzy rule based classifier is designed for efficient decision making for data classification in the proposed scheme. To perform inferencing from the collected data, membership functions are designed for fuzzification and defuzzification processes. The proposed scheme is evaluated on various evaluation metrics, such as average response time, accuracy, computation cost, classification time, and false positive ratio. The results obtained confirm the effectiveness of the proposed scheme with respect to various performance evaluation metrics in cloud computing environment.
随着信息和通信技术的进步，远程医疗应用也在急剧增加，患者也可以在远程获得治疗。远程医疗应用程序收集的有关患者的数据构成大数据，因为它具有体积、速度、种类、准确性和价值的变化。处理如此大量的异类数据是最大的挑战之一，需要专门的方法。为了解决这一挑战，本文提出了一种新的基于模糊规则的分类器，旨在提供医疗保健即服务。该方案基于初始聚类形成、大数据在云环境中的检索和处理。然后，为了在提出的方案中实现数据分类的有效决策，设计了基于模糊规则的分类器。为了从收集的数据中进行推断，设计了隶属函数进行模糊化和去模糊化处理。该方案在各种评估指标（如平均响应时间、准确性、计算成本、分类时间和误报率）上进行了评估。在云计算环境中，所获得的结果通过各种性能评估指标证实了该方案的有效性。
