Providing Healthcare-as-a-Service Using Fuzzy Rule-Based Big Data Analytics in Cloud Computing
Abstract:
With advancements in information and communication technology (ICT), there is steep increase in the remote healthcare applications in which patients get treatment from the remote places also. The data collected about the patients in remote healthcare applications constitutes to big data because it varies with respect to volume, velocity, variety, veracity, and value. To process such a large collection of heterogeneous data is one of the biggest challenges that needs 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 (HaaS)1 . The proposed scheme is based upon the initial cluster formation, retrieval, and processing of the big data in the cloud environment. Then, a fuzzy rule-based classifier is designed for efficient decision making about the 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.
Existing System:
Cloud computing is one of the emerging technologies for handling the big data generated from various applications and to construct a decision support system so that extracted data can easily be accessed from anywhere. There are lot of proposals reported in the literature addressing the issues of remote healthcare and big data analytics.
Proposed System:
the purpose of providing healthcare services, we have used a modification of standardized Expectation-Maximization (EM) algorithm [9] to store the data on cloud by considering different clouds based upon different data clusters. Then, a fuzzy rulebased classifier is proposed for storing fuzzy values and to retrieve the data from the cloud with reduced response time and high throughput. This paper is an extension of our preliminary work that has been reported in with detailed description of each working phase. Moreover, the proposed scheme has been rigorously tested on various evaluation metrics at different simulation settings and compared with benchmark schemes in both centralized as well as distributed cloud environments.
CONCLUSION:
As healthcare applications generate large amount of data which varies with respect to its volume, variety, velocity, veracity, and value, there is an imminent requirement of efficient mining techniques for context-aware retrieval and processing of this class of data. This paper proposes a new fuzzy rulebased classifier to provide Healthcare-as-a-Service (HaaS) and to classify the big data generated in this environment. The proposed scheme uses cloud-based infrastructure as a repository for storage and applying analytical algorithms for retrieval of information about the patients. To apply analytics, algorithms for cluster formation and data retrieval are designed on the basis of Expectation-Maximization and fuzzy rule-based classifier. The proposed approach is compared with existing schemes and its performance is analyzed with respect to various evaluation metrics namely-average response time, accuracy, computation cost, classification time and false positive rate. The results obtained show that the proposed scheme is effective in finding out the probable patients suffering from a particular disease. Moreover, the proposed scheme performed better when compared with its counterparts namely multi-layer, Bayes network and decision table in terms of classification time and false positive rate.
REFERENCES
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