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BioMed Research International
Volume 2017, Article ID 3292849, 12 pages
Research Article

Improving Prediction Accuracy of “Central Line-Associated Blood Stream Infections” Using Data Mining Models

1Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
2Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
3Clinical Epidemiology & Infection Control, Faculty of Nursing, King Abdulaziz University, Jeddah, Saudi Arabia
4Department of Computer Science and Software Engineering, University of Hail, Hail, Saudi Arabia

Correspondence should be addressed to Abdul Hamid M. Ragab; as.ude.uak@bagara

Received 1 March 2017; Revised 23 July 2017; Accepted 30 July 2017; Published 20 September 2017

Academic Editor: Hesham H. Ali

Copyright © 2017 Amin Y. Noaman et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Prediction of nosocomial infections among patients is an important part of clinical surveillance programs to enable the related personnel to take preventive actions in advance. Designing a clinical surveillance program with capability of predicting nosocomial infections is a challenging task due to several reasons, including high dimensionality of medical data, heterogenous data representation, and special knowledge required to extract patterns for prediction. In this paper, we present details of six data mining methods implemented using cross industry standard process for data mining to predict central line-associated blood stream infections. For our study, we selected datasets of healthcare-associated infections from US National Healthcare Safety Network and consumer survey data from Hospital Consumer Assessment of Healthcare Providers and Systems. Our experiments show that central line-associated blood stream infections (CLABSIs) can be successfully predicted using AdaBoost method with an accuracy up to 89.7%. This will help in implementing effective clinical surveillance programs for infection control, as well as improving the accuracy detection of CLABSIs. Also, this reduces patients’ hospital stay cost and maintains patients’ safety.