Big Data Analytics in E-Healthcare Using Hadoop and Hive
Title
Big Data Analytics in E-Healthcare Using Hadoop and Hive
Subject
Big data
Predictive analytics
Data Analytics
Smoke
Diseases
Diagnosis
Patient treatment
Disease control
Biological organs
Hospital data processing
Description
New technologies like big data, cloud computing, machine learning could play a vital role in providing healthcare services to patients. The healthcare industry is producing a large volume of data which is increasing exponentially. Healthcare industry data is unstructured and is collected in different varieties. There is a dire need of processing these huge volumes of healthcare datasets to provide personalized treatment to the patients, to provide predictive analytics so the pre-diagnosis can take place. To predict the insights from existing patient data requires new tools and techniques as healthcare data is complex. Big data technologies such as Hadoop, MapReduce, Pig, Hive, and others provide the platform for healthcare data processing. Increasing rates of severe health diseases are impacting human life. One such kind of disease is cancer, this work presents the association between smoking and lung cancer. This paper presents the implementation of hive architecture for analyzing the lung cancer rate among active smokers dataset from centers of disease control and prevention government agency. The results obtained show that active smokers have a higher rate of lung cancer. It also addresses various challenges of implementing big data techniques over healthcare data. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
951-965
936
Creator
Choudhary, Richa
Publisher
4th International Conference on Futuristic Trends in Networks and Computing Technologies, FTNCT 2021, December 10, 2021 - December 11, 2021
Date
2022
Type
conferencePaper
Identifier
18761100
10.1007/978-981-19-5037-7_68
Citation
Choudhary, Richa, “Big Data Analytics in E-Healthcare Using Hadoop and Hive,” Lamar University Midstream Center Research, accessed May 4, 2024, https://lumc.omeka.net/items/show/29392.