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Intelligent hypothermia care system using ant colony optimization for rules prediction
Author
al-Buhayli, Haydar Nasir Khurayba
Source
Journal of Babylon University : Journal of Applied and Pure Sciences
Issue
Vol. 26, Issue 2 (28 Feb. 2018), pp.47-56, 10 p.
Publisher
Publication Date
2018-02-28
Country of Publication
Iraq
No. of Pages
10
Main Subjects
Information Technology and Computer Science
Abstract EN
Intelligent Hypothermia Care System (IHCS) is an intelligence system uses set of methodologies, algorithms, architectures and processes to determine where patients in a postoperative recovery area must be sent.
Hypothermia is a significant concern after surgery.
This paper utilizes the classification task in data mining to propose an intelligent technique to predict where to send a patient after surgery: intensive care unit, general floor or home.
To achieve this goal, this paper evaluates the performance of decision tree algorithm, exemplifying the deterministic approach, against the AntMiner algorithm, exemplifying the heuristic approach, to choose the best approach in detecting the patient’s status.
Results show the outperformance of the heuristic approach.
The implication of this proposal will be twofold: in hypothermia treatment and in the application of ant colony optimization.-
American Psychological Association (APA)
al-Buhayli, Haydar Nasir Khurayba. 2018. Intelligent hypothermia care system using ant colony optimization for rules prediction. Journal of Babylon University : Journal of Applied and Pure Sciences،Vol. 26, no. 2, pp.47-56.
https://search.emarefa.net/detail/BIM-1094078
Modern Language Association (MLA)
al-Buhayli, Haydar Nasir Khurayba. Intelligent hypothermia care system using ant colony optimization for rules prediction. Journal of Babylon University : Journal of Applied and Pure Sciences Vol. 26, no. 2 (2018), pp.47-56.
https://search.emarefa.net/detail/BIM-1094078
American Medical Association (AMA)
al-Buhayli, Haydar Nasir Khurayba. Intelligent hypothermia care system using ant colony optimization for rules prediction. Journal of Babylon University : Journal of Applied and Pure Sciences. 2018. Vol. 26, no. 2, pp.47-56.
https://search.emarefa.net/detail/BIM-1094078
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 55-56
Record ID
BIM-1094078