Title

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

Document Type

Article

Publication Date

2019

Keywords

Clustering, Decision support systems, Healthcare management, Data mining, Business analytics

Disciplines

Business Analytics

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.

Publication Title

Industrial Management and Data Systems

Volume

119

Issue

1

First Page

189

Last Page

209

ISSN

0263-5577

DOI

10.1108/IMDS-12-2017-0579

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