Open Access
Subscription Access
Healthcare Analytics Using Big Data for Evaluation and Extreme Machine Learning Based on MapReduce
Subscribe/Renew Journal
The present study focuses on Extreme Machine Learning Method (ELM), which may be used with Support Vector Machines that are optimised by Cuckoo Search to produce a method for identifying disease risk (CS-SVM). It also considers the accuracy and scalability of big data models, which considerably increase the processing power of the proposed method and produce better outcomes in terms of performance metrics like veracity and efficiency. In terms of additional performance metrics like Precision, Recall, and Average Area Under Curve, the suggested method is also compared to comparable cutting-edge methods.
Keywords
Big Data, Extreme Machine Learning.
User
Subscription
Login to verify subscription
Font Size
Information
- F. O. Catak, "Classification with Extreme Learning Machine and ensemble algorithms over randomly partitioned data," in 23rd Signal Process. Commun. Appl. Conf., 2015, pp. 228-231, doi: 10.1109/SIU.2015.7129801.
- J. -H. Zhai, S. -F. Zhang, M. -H. Wang, and Y. Li, "A three-stage method for classification of binary imbalanced big data," in 2020 Int. Conf. Mach. Leam. Adelaide, Australia, 2020, pp. 207-212, doi: 10.1109/ICMLC51923.2020.9469568.
- R Nair and I. Kashyap, "Hybrid pre-processing technique for handling imbalanced data and detecting outliers for KNN classifier," in 2019 Int. Conf. Mach. Learn., Big Data, Cloud Parallel Comput., 2019, pp. 460-464, doi: 10.1109/COMITCon.2019.8862250.
- M. S. Hossain and G. Muhammad, "Healthcare Big Data Voice Pathology Assessment Framework," in IEEE Access, vol. 4, pp. 7806-7815, 2016, doi: 10.1109/ACCESS.2016.262631.
- H. Wen and F. Huang, "Personal loan fraud detection based on hybrid supervised and unsupervised learning," in 2020 5th IEEE Int. Conf. Big Data Analytics, 2020, pp. 339-343, doi: 10.1109/ICBDA49040.2020.9101277.
- S. Y. Choi and K. Chung, “Knowledge process of health big data using MapReduce-based associative mining,” Personal Ubiquitious Comput., vol. 24, no. 5, pp.571-581,2020.
- M. Herland, T. M. Khoshgoftaar, and R. Wald, “A review of data mining using big data in health informatics,” J. Big Data, 1, Article No. 2, 2014, doi: 10.1186/2196-1115-1-2.
- C. K.-S. Leung, R. K. MacKinnon, and F. Jiang, “Finding efficiencies in frequent pattern mining from big uncertain data,” World Wide Web, vol. 20, no. 3, pp.571-594,2017.
- H. Guo, H. Liu, J. Y. Chen, and Y. Zeng, “Data mining and risk prediction based on apriori improved algorithm for lung cancer,” J. Sign Process Syst., vol. 93, pp. 795-809,2021, doi: 10.1007/11265-021-01663-1.
- P. K. Mantha, A. Luckow, and S. Jha, “Pilot-MapReduce: An extensible and flexible MapReduce implementation for distributed data,” in Proc. 3rd Int. Workshop MapReduce Appl. Date, Association for Computing Machinery, New York, NY, USA, pp. 17-24, 2012, doi: https://doi.org/10.1145/2287016.2287020.
- P. Deligiannis, L. Hans-Wolfgang, and E. Kouidi, “Improving the diagnosis of mild hypertrophic cardiomyopathy with MapReduce,” in Proc. 3rd Int. Workshop MapReduce Appl. Date (MapReduce '12), Association for Computing Machinery, New York, NY, USA, 2012, pp. 41-48, doi: 10.1145/2287016.2287025.
- Y. Wu, L. Zheng, B. Heilig, and G. R. Gao, “Design and evaluation of a novel dataflow based bigdata solution,” in Proc. 6th Int. Workshop Program. Models Appl. Multicores Manycores, Association for Computing Machinery, New York, NY, USA, 2015, pp. 40-48, doi: 10.1145/2712386.2712397.
Abstract Views: 123
PDF Views: 0