![CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
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.](https://image.slidesharecdn.com/providinghealthcare-as-a-serviceusingfuzzyrule-basedbigdataanalyticsincloudcomputing-181021095326/85/Providing-healthcare-as-a-service-using-fuzzy-rule-based-big-data-analytics-in-cloud-computing-1-320.jpg)
![CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
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
[1] C.W. Tsai, C.F. Lai, H.C. Chao, A.V. Vasilakos, “Big Data Analytics: A Survey,”
Journal of Big Data, vol. 2, no. 1, pp. 1-32, 2015.
[2] Y. Zhang, M. Qiu, C.-W. Tsai, M. M. Hassan, and A. Alamri, “HealthCPS: Healthcare
Cyber-Physical System Assisted by Cloud and Big Data,” IEEE Systems Journal, vol. 11, no.
1, pp. 88-95, 2017.
[3] N. Kumar, K. Kaur, A. Jindal, and J.J.P.C. Rodrigues, “Providing Healthcare Services
On-the-Fly Using Multi-player Cooperation Game Theory in Internet of Vehicles (IoV)
Environment,” Digital Communications and Networks, vol. 1, no. 3, pp. 191-203, 2015.
[4] S. Saria,“A $3 Trillion Challenge to Computational Scientists: Transforming Healthcare
Delivery,” IEEE Intelligent Systems, vol. 4, pp. 82-87, 2014.
[5] M. Chen, S. Gonzalez, A. Vasilakos, H. Cao, and V.C. Leung, “Body Area Networks: A
Survey,” Mobile Networks and Applications, vol. 16, no. 2, pp. 171-193, 2011.
[6] G. Fortino, G.D. Fatta, M. Pathan, and A.V. Vasilakos, “Cloud-assisted body area
networks: state-of-the-art and future challenges,” Wireless Networks, vol. 20, no. 7, pp.
1925-1938, 2014.](https://image.slidesharecdn.com/providinghealthcare-as-a-serviceusingfuzzyrule-basedbigdataanalyticsincloudcomputing-181021095326/85/Providing-healthcare-as-a-service-using-fuzzy-rule-based-big-data-analytics-in-cloud-computing-2-320.jpg)
![CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
[7] D. Talia, “Clouds for Scalable Big Data Analytics,” IEEE Computer Magazine, vol. 46,
no. 5, pp. 98-101, 2013.](https://image.slidesharecdn.com/providinghealthcare-as-a-serviceusingfuzzyrule-basedbigdataanalyticsincloudcomputing-181021095326/85/Providing-healthcare-as-a-service-using-fuzzy-rule-based-big-data-analytics-in-cloud-computing-3-320.jpg)
Providing healthcare as-a-service using fuzzy rule-based big data analytics in cloud computing
- 1. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249) MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com 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.
- 2. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249) MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com 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 [1] C.W. Tsai, C.F. Lai, H.C. Chao, A.V. Vasilakos, “Big Data Analytics: A Survey,” Journal of Big Data, vol. 2, no. 1, pp. 1-32, 2015. [2] Y. Zhang, M. Qiu, C.-W. Tsai, M. M. Hassan, and A. Alamri, “HealthCPS: Healthcare Cyber-Physical System Assisted by Cloud and Big Data,” IEEE Systems Journal, vol. 11, no. 1, pp. 88-95, 2017. [3] N. Kumar, K. Kaur, A. Jindal, and J.J.P.C. Rodrigues, “Providing Healthcare Services On-the-Fly Using Multi-player Cooperation Game Theory in Internet of Vehicles (IoV) Environment,” Digital Communications and Networks, vol. 1, no. 3, pp. 191-203, 2015. [4] S. Saria,“A $3 Trillion Challenge to Computational Scientists: Transforming Healthcare Delivery,” IEEE Intelligent Systems, vol. 4, pp. 82-87, 2014. [5] M. Chen, S. Gonzalez, A. Vasilakos, H. Cao, and V.C. Leung, “Body Area Networks: A Survey,” Mobile Networks and Applications, vol. 16, no. 2, pp. 171-193, 2011. [6] G. Fortino, G.D. Fatta, M. Pathan, and A.V. Vasilakos, “Cloud-assisted body area networks: state-of-the-art and future challenges,” Wireless Networks, vol. 20, no. 7, pp. 1925-1938, 2014.
- 3. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249) MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com [7] D. Talia, “Clouds for Scalable Big Data Analytics,” IEEE Computer Magazine, vol. 46, no. 5, pp. 98-101, 2013.