Big Data for Health Care Analytics using Extreme Machine Learning Based on Map Reduce
| Autor: | Revathy Ondimuthu, N. S. Nithya, Sivakumar Karuppan |
|---|---|
| Přispěvatelé: | Blue Eyes Intelligence Engineering & Sciences Publication (BEIESP) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Environmental Engineering
Computer science business.industry Map reduce Machine Learning Big Data Analytics EHR CS-SVM Big data General Engineering 2249-8958 Machine learning computer.software_genre Computer Science Applications Analytics C5808029320/2020©BEIESP Map reduce Health care Artificial intelligence business computer |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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