Comparison of predictive models for hospital readmission of heart failure patients with cost-sensitive approach

ABSTRACT

Readmission rates for heart failure patients remain high but it is potentially preventable. Many predictive models have been developed over the years to identify heart failure patients who are at high risk of readmission but only a few of them incorporate cost considerations. The goal of this study is to compare the performance of four machine learning algorithms in predicting the readmission of heart failure patients with cost consideration. We also aim to identify the risk factors associated with a patient’s readmission within one year of a retrospective cohort study. The best model selection was found after four machine-learning methods were tested; these include logistic regression, support vector machine, random forest and neural network. The study found a support vector machine to have the best prediction performance with an AUC score of 0.602. The model showed twelve (12) predictors that are significantly associated with the identification of heart failure patients at high risk of readmission.

KEYWORDS:

• Heart failure
• machine learning
• hospital readmission
• cost-sensitive learning
• predictive analytics

Disclosure statement

No potential conflict of interest was reported by the author(s).

Ethics approval

The Northern Mindanao medical Center research Ethics Board approved this study.

Additional information

Notes on contributors

Junar Arciete Landicho

Junar Arciete Landicho is a PhD candidate at the Asian Institute of Technology. He earned his master’s degree in Information Technology at the Mindanao University of Science and Technology, where he is now a faculty member. His research interest is in the field of database system, mobile application, image and sound processing.

Vatcharaporn Esichaikul

Vatcharaporn Esichaikul is an Associate Professor of Information Management at the Asian Institute of Technology in Thailand. She received her PhD in Management Information System from Kent State University in the United States. Her interests include research in digital learning and mobile learning.

Roy Magdugo Sasil

Roy Magdugo Sasil is a medical doctor and currently the head of non-invasive cardiology at the Northern Mindanao Medical Center in Cagayan de Oro City, Philippines. His specialization is adult cardiology with expertise in 2d, 3d and 4d echocardiography. He performs stress and pharmacologic stress echocardiography as well as transesophageal and intraprocedural echocardiography.