Resource-efficient fast prediction in healthcare data analytics: A pruned Random Forest regression approach

Fawagreh, Khaled and Gaber, Mohamed Medhat (2020) Resource-efficient fast prediction in healthcare data analytics: A pruned Random Forest regression approach. Computing. ISSN 0010-485X

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Abstract

In predictive healthcare data analytics, high accuracy is both vital and paramount as low accuracy can lead to misdiagnosis, which is known to cause serious health consequences or death. Fast prediction is also considered an important desideratum particularly for machines and mobile devices with limited memory and processing power. For real-time health care analytics applications, particularly the ones that run on mobile devices, such traits (high accuracy and fast prediction) are highly desirable. In this paper, we propose to use an ensemble regression technique based on CLUB-DRF, which is a pruned Random Forest that possesses these features. The speed and accuracy of the method have been demonstrated by an experimental study on three medical data sets of three different diseases.

Item Type: Article
Identification Number: https://doi.org/10.1007/s00607-019-00785-6
Date: 9 January 2020
Uncontrolled Keywords: Random Forest; Healthcare data analytics; CLUB-DRF; Ensemble classification; Ensemble pruning
Subjects: G700 Artificial Intelligence
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Mohamed Gaber
Date Deposited: 10 Jan 2020 15:21
Last Modified: 09 Oct 2020 10:17
URI: http://www.open-access.bcu.ac.uk/id/eprint/8700

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