To read this content please select one of the options below:

A comparative data analytic approach to construct a risk trade-off for cardiac patients’ re-admissions

Murtaza Nasir (Department of Decision Science, School of Business, University of South Dakota, Vermillion, South Dakota, USA)
Carole South-Winter (Department of Health Services Administration, School of Business, University of South Dakota, Vermillion, South Dakota, USA)
Srini Ragothaman (Department of Accounting & Finance, School of Business, University of South Dakota, Vermillion, South Dakota, USA)
Ali Dag (Department of Decision Science, School of Business, University of South Dakota, Vermillion, South Dakota, USA)

Industrial Management & Data Systems

ISSN: 0263-5577

Article publication date: 23 August 2018

Issue publication date: 8 February 2019

260

Abstract

Purpose

The purpose of this paper is to formulate a framework to construct a patient-specific risk score and therefore to classify these patients into various risk groups that can be used as a decision support mechanism by the medical decision makers to augment their decision-making process, allowing them to optimally use the limited resources available.

Design/methodology/approach

A conventional statistical model (logistic regression) and two machine learning-based (i.e. artificial neural networks (ANNs) and support vector machines) data mining models were employed by also using five-fold cross-validation in the classification phase. In order to overcome the data imbalance problem, random undersampling technique was utilized. After constructing the patient-specific risk score, k-means clustering algorithm was employed to group these patients into risk groups.

Findings

Results showed that the ANN model achieved the best results with an area under the curve score of 0.867, while the sensitivity and specificity were 0.715 and 0.892, respectively. Also, the construction of patient-specific risk scores offer useful insights to the medical experts, by helping them find a trade-off between risks, costs and resources.

Originality/value

The study contributes to the existing body of knowledge by constructing a framework that can be utilized to determine the risk level of the targeted patient, by employing data mining-based predictive approach.

Keywords

Citation

Nasir, M., South-Winter, C., Ragothaman, S. and Dag, A. (2019), "A comparative data analytic approach to construct a risk trade-off for cardiac patients’ re-admissions", Industrial Management & Data Systems, Vol. 119 No. 1, pp. 189-209. https://doi.org/10.1108/IMDS-12-2017-0579

Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited

Related articles