Proceedings of the 2016 International Conference on Artificial Intelligence and Engineering Applications

Medical health data analysis based on Spark Mllib

Authors
Tong Xiao, Yijie Shi
Corresponding Author
Tong Xiao
Available Online November 2016.
DOI
https://doi.org/10.2991/aiea-16.2016.21How to use a DOI?
Keywords
Machine learning; Medical wisdom; Health records; Classification.
Abstract

In recent years, health and disease prediction has become an important part of medical wisdom, and has attracted more and more attention. At this stage, the prediction of health care mainly relies on the medical health records data. For predicting results, it is only in view of the disease or not. At present, for the lack of adaptability and limitations of the data feature selection, in this paper we use the existing health records data and available life habit data, combined with the current popular Spark machine learning computing platform, and establish a multi-classification model, which can provide a reasonable prediction and evaluation. This design has a certain degree of accuracy and efficiency and it has a certain use value.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2016 International Conference on Artificial Intelligence and Engineering Applications
Series
Advances in Computer Science Research
Publication Date
November 2016
ISBN
978-94-6252-270-1
ISSN
2352-538X
DOI
https://doi.org/10.2991/aiea-16.2016.21How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Tong Xiao
AU  - Yijie Shi
PY  - 2016/11
DA  - 2016/11
TI  - Medical health data analysis based on Spark Mllib
BT  - Proceedings of the 2016 International Conference on Artificial Intelligence and Engineering Applications
PB  - Atlantis Press
SP  - 116
EP  - 119
SN  - 2352-538X
UR  - https://doi.org/10.2991/aiea-16.2016.21
DO  - https://doi.org/10.2991/aiea-16.2016.21
ID  - Xiao2016/11
ER  -