Big Data for Health Care Analytics using Extreme Machine Learning Based on Map Reduce
Sivakumar Karuppan1, N. S. Nithya2, Revathy Ondimuthu3
1Sivakumar Karuppan*, Assistant Professor, Department of CSE, JCT College of Engineering and Technology, Coimbatore, India.
2Dr. N. S. Nithya, Associate Professor, Department of CSE, KSR College of Engineering , Tiruchengode, India.
3Revathy Ondimuthu, Assistant Professor, Department of ECE, Hindustan College of Engineering , and Technology, Coimbatore, India.
Manuscript received on January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 2758-2762 | Volume-9 Issue-3, February 2020. | Retrieval Number: C5808029320/2020©BEIESP | DOI: 10.35940/ijeat.C5808.029320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: A large volume of datasets is available in various fields that are stored to be somewhere which is called big data. Big Data healthcare has clinical data set of every patient records in huge amount and they are maintained by Electronic Health Records (EHR). More than 80 % of clinical data is the unstructured format and reposit in hundreds of forms. The challenges and demand for data storage, analysis is to handling large datasets in terms of efficiency and scalability. Hadoop Map reduces framework uses big data to store and operate any kinds of data speedily. It is not solely meant for storage system however conjointly a platform for information storage moreover as processing. It is scalable and fault-tolerant to the systems. Also, the prediction of the data sets is handled by machine learning algorithm. This work focuses on the Extreme Machine Learning algorithm (ELM) that can utilize the optimized way of finding a solution to find disease risk prediction by combining ELM with Cuckoo Search optimization-based Support Vector Machine (CS-SVM). The proposed work also considers the scalability and accuracy of big data models, thus the proposed algorithm greatly achieves the computing work and got good results in performance of both veracity and efficiency.
Keywords: Map reduce, Machine Learning, Big Data Analytics, EHR, CS-SVM.