Big Data for Health Care Analytics using Extreme Machine Learning Based on Map Reduce

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 uns...

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Published in:International journal of engineering and advanced technology Vol. 9; no. 3; pp. 2758 - 2762
Main Authors: Karuppan, Sivakumar, Nithya, N. S., Ondimuthu, Revathy
Format: Journal Article
Language:English
Published: 28.02.2020
ISSN:2249-8958, 2249-8958
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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.
AbstractList 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.
Author Nithya, N. S.
Ondimuthu, Revathy
Karuppan, Sivakumar
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CorporateAuthor Assistant Professor, Department of CSE, JCT College of Engineering and Technology, Coimbatore, India
Associate Professor, Department of CSE, KSR College of Engineering , Tiruchengode, India
Assistant Professor, Department of ECE, Hindustan College of Engineering , and Technology, Coimbatore, India
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– name: Assistant Professor, Department of ECE, Hindustan College of Engineering , and Technology, Coimbatore, India
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