IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Machine Learning - Special issue on learning with probabilistic representations
A Bayesian Approach to Joint Feature Selection and Classifier Design
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Bayesian Networks
Genetic programming for epileptic pattern recognition in electroencephalographic signals
Applied Soft Computing
Evolutionary computing in manufacturing industry: an overview of recent applications
Applied Soft Computing
ECG data compression using truncated singular value decomposition
IEEE Transactions on Information Technology in Biomedicine
An ischemia detection method based on artificial neural networks
Artificial Intelligence in Medicine
An interactive framework for an analysis of ECG signals
Artificial Intelligence in Medicine
An arrhythmia classification system based on the RR-interval signal
Artificial Intelligence in Medicine
On the selection and classification of independent features
IEEE Transactions on Pattern Analysis and Machine Intelligence
EURASIP Journal on Advances in Signal Processing
Expert Systems with Applications: An International Journal
COMPUTE '11 Proceedings of the Fourth Annual ACM Bangalore Conference
An adaptive binary PSO to learn bayesian classifier for prognostic modeling of metabolic syndrome
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Expert Systems with Applications: An International Journal
Estimation of heart rate signals for mental stress assessment using neuro fuzzy technique
Applied Soft Computing
ECG arrhythmia classification based on optimum-path forest
Expert Systems with Applications: An International Journal
Artificial Intelligence in Medicine
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
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Objective: To classify patients by age based upon information extracted from their electrocardiograms (ECGs). To develop and compare the performance of Bayesian classifiers. Methods and material: We present a methodology for classifying patients according to statistical features extracted from their ECG signals using a genetically evolved Bayesian network classifier. Continuous signal feature variables are converted to a discrete symbolic form by thresholding, to lower the dimensionality of the signal. This simplifies calculation of conditional probability tables for the classifier, and makes the tables smaller. Two methods of network discovery from data were developed and compared: the first using a greedy hill-climb search and the second employed evolutionary computing using a genetic algorithm (GA). Results and conclusions: The evolved Bayesian network performed better (86.25% AUC) than both the one developed using the greedy algorithm (65% AUC) and the naive Bayesian classifier (84.75% AUC). The methodology for evolving the Bayesian classifier can be used to evolve Bayesian networks in general thereby identifying the dependencies among the variables of interest. Those dependencies are assumed to be non-existent by naive Bayesian classifiers. Such a classifier can then be used for medical applications for diagnosis and prediction purposes.