Providing Healthcare-as-a-Service Using Fuzzy Rule Based Big Data Analytics in Cloud Computing

被引:46
|
作者
Jindal, Anish [1 ]
Dua, Amit [2 ]
Kumar, Neeraj [1 ]
Das, Ashok Kumar [3 ]
Vasilakos, Athanasios V. [4 ]
Rodrigues, Joel J. P. C. [5 ,6 ,7 ,8 ]
机构
[1] Thapar Inst Engn & Technol, Dept Comp Sci & Engn, Patiala 147004, Punjab, India
[2] Birla Inst Technol & Sci, Dept Comp Sci & Informat Syst, Pilani 333031, Rajasthan, India
[3] Int Inst Informat Technol, Ctr Secur Theory & Algorithm Res, Hyderabad 500032, India
[4] Innopolis Univ, Innopolis 420500, Russia
[5] Natl Inst Telecommun Inatel, BR-37540000 Santa Rita Do Sapucal, Brazil
[6] Inst Telecomunicacoes, P-1049001 Lisbon, Portugal
[7] ITMO Univ, St Petersburg 197101, Russia
[8] Univ Fortaleza UNIFOR, BR-60811905 Fortaleza, Ceara, Brazil
关键词
Big data analytics; cloud computing environment; fuzzy rule-based classifier; healthcare applications;
D O I
10.1109/JBHI.2018.2799198
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:1605 / 1618
页数:14
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