Journal article
Machine learning-based prediction of heart failure readmission or death: Implications of choosing the right model and the right metrics
ESC Heart Failure, Vol.6(2), pp.428-435
2019
Abstract
Aims
Machine learning (ML) is widely believed to be able to learn complex hidden interactions from the data and has the potential in predicting events such as heart failure (HF) readmission and death. Recent studies have revealed conflicting results likely due to failure to take into account the class imbalance problem commonly seen with medical data. We developed a new ML approach to predict 30 day HF readmission or death and compared the performance of this model with other commonly used prediction models.
Methods and results
We identified all Western Australian patients aged above 65 years admitted for HF between 2003 and 2008 in the linked Hospital Morbidity Data Collection. Taking into consideration the class imbalance problem, we developed a multi‐layer perceptron (MLP)‐based approach to predict 30 day HF readmission or death and compared the predictive performances using the performance metrics, that is, area under the receiver operating characteristic curve (AUC), area under the precision–recall curve (AUPRC), sensitivity and specificity with other ML and regression models. Out of the 10 757 patients with HF, 23.6% were readmitted or died within 30 days of hospital discharge. We observed an AUC of 0.55, 0.53, 0.58, and 0.54 while an AUPRC of 0.39, 0.38, 0.46, and 0.38 for weighted random forest, weighted decision trees, logistic regression, and weighted support vector machines models, respectively. The MLP‐based approach produced the highest AUC (0.62) and AUPRC (0.46) with 48% sensitivity and 70% specificity.
Conclusions
We show that for the medical data with class imbalance, the proposed MLP‐based approach is superior to other ML and regression techniques for the prediction of 30 day HF readmission or death.
Details
- Title
- Machine learning-based prediction of heart failure readmission or death: Implications of choosing the right model and the right metrics
- Authors/Creators
- S.E. Awan (Author/Creator) - The University of Western AustraliaM. Bennamoun (Author/Creator) - The University of Western AustraliaF. Sohel (Author/Creator) - Murdoch UniversityF.M. Sanfilippo (Author/Creator) - The University of Western AustraliaG. Dwivedi (Author/Creator) - Harry Perkins Institute of Medical Research
- Publication Details
- ESC Heart Failure, Vol.6(2), pp.428-435
- Publisher
- John Wiley & Sons Ltd
- Identifiers
- 991005544981007891
- Copyright
- © 2019 The Authors.
- Murdoch Affiliation
- School of Engineering and Information Technology
- Language
- English
- Resource Type
- Journal article
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Source: InCites
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- Collaboration types
- Domestic collaboration
- Citation topics
- 1 Clinical & Life Sciences
- 1.37 Cardiology - General
- 1.37.328 Heart Failure Management
- Web Of Science research areas
- Cardiac & Cardiovascular Systems
- ESI research areas
- Clinical Medicine