Application of the RIMARC algorithm to a large data set of action potentials and clinical parameters for risk prediction of atrial fibrillation
Author(s)
Date
2015Source Title
Medical & Biological Engineering & Computing
Print ISSN
0140-0118
Electronic ISSN
1741-0444
Publisher
Springer
Volume
53
Issue
3
Pages
263 - 273
Language
English
Type
ArticleItem Usage Stats
254
views
views
283
downloads
downloads
Abstract
Ex vivo recorded action potentials (APs) in human right atrial tissue from patients in sinus rhythm (SR) or atrial fibrillation (AF) display a characteristic spike-and-dome or triangular shape, respectively, but variability is huge within each rhythm group. The aim of our study was to apply the machine-learning algorithm ranking instances by maximizing the area under the ROC curve (RIMARC) to a large data set of 480 APs combined with retrospectively collected general clinical parameters and to test whether the rules learned by the RIMARC algorithm can be used for accurately classifying the preoperative rhythm status. APs were included from 221 SR and 158 AF patients. During a learning phase, the RIMARC algorithm established a ranking order of 62 features by predictive value for SR or AF. The model was then challenged with an additional test set of features from 28 patients in whom rhythm status was blinded. The accuracy of the risk prediction for AF by the model was very good (0.93) when all features were used. Without the seven AP features, accuracy still reached 0.71. In conclusion, we have shown that training the machine-learning algorithm RIMARC with an experimental and clinical data set allows predicting a classification in a test data set with high accuracy. In a clinical setting, this approach may prove useful for finding hypothesis-generating associations between different parameters.
Keywords
Human right atrial action potentialsRIMARC algorithm
Risk prediction
Artificial intelligence
Diseases
Electrophysiology
Forecasting
Learning algorithms
Learning systems
Parameter estimation
Social aspects
Statistical tests
Area under the ROC curve
Clinical parameters
Clinical settings
Triangular shapes
Classification (of information)
Adult
Algorithm
Anthropometric parameters
Area under the curve
Cardiovascular risk
Clinical classification
Clinical feature
Clinical study
Disease association
Electrophysiological procedures
Ex vivo study
Female
Health status
Heart rhythm
Hemodynamic parameters
Human
Machine learning
Major clinical study
Measurement accuracy
Open heart surgery
Patient risk
Predictive value
Priority journal
Ranking instances by maximizing the area under the roc curve
Receiver operating characteristic
Retrospective study
Risk assessment
Risk factor
Action potential
Atrial fibrillation
Heart atrium
Pathophysiology
Physiology
Aged
Male
Risk
ROC curve
Permalink
http://hdl.handle.net/11693/26684Published Version (Please cite this version)
http://dx.doi.org/10.1007/s11517-014-1232-0Collections
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