Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Use of Support Vector Machines and Neural Network in Diagnosis of Neuromuscular Disorders
Journal of Medical Systems
Journal of Biomedical Informatics
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Model gene network by semi-fixed Bayesian network
Expert Systems with Applications: An International Journal
Transmembrane segments prediction and understanding using support vector machine and decision tree
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A multilayer perceptron-based medical decision support system for heart disease diagnosis
Expert Systems with Applications: An International Journal
WeAidU-a decision support system for myocardial perfusion images using artificial neural networks
Artificial Intelligence in Medicine
Model selection for a medical diagnostic decision support system: a breast cancer detection case
Artificial Intelligence in Medicine
Empirical analysis of support vector machine ensemble classifiers
Expert Systems with Applications: An International Journal
Specializing for predicting obesity and its co-morbidities
Journal of Biomedical Informatics
Probabilistic self-organizing maps for continuous data
IEEE Transactions on Neural Networks
A predictive model for cerebrovascular disease using data mining
Expert Systems with Applications: An International Journal
Computer Methods and Programs in Biomedicine
Expert Systems with Applications: An International Journal
Risk prediction for postoperative morbidity of endovascular aneurysm repair using ensemble model
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part III
Classifiers selection in ensembles using genetic algorithms for bankruptcy prediction
Expert Systems with Applications: An International Journal
Technological Innovations in the Development of Cardiovascular Clinical Information Systems
Journal of Medical Systems
Elucidating clinical context of lymphopenia by nonlinear modelling
Expert Systems with Applications: An International Journal
Journal of Medical Systems
Artificial intelligence models to stratify cardiovascular risk in incident hemodialysis patients
Expert Systems with Applications: An International Journal
Pattern Recognition Letters
A survey of multiple classifier systems as hybrid systems
Information Fusion
Hi-index | 12.06 |
Conventional clinical decision support systems are generally based on a single classifier or a simple combination of these models, showing moderate performance. In this paper, we propose a classifier ensemble-based method for supporting the diagnosis of cardiovascular disease (CVD) based on aptamer chips. This AptaCDSS-E system overcomes conventional performance limitations by utilizing ensembles of different classifiers. Recent surveys show that CVD is one of the leading causes of death and that significant life savings can be achieved if precise diagnosis can be made. For CVD diagnosis, our system combines a set of four different classifiers with ensembles. Support vector machines and neural networks are adopted as base classifiers. Decision trees and Bayesian networks are also adopted to augment the system. Four aptamer-based biochip data sets including CVD data containing 66 samples were used to train and test the system. Three other supplementary data sets are used to alleviate data insufficiency. We investigated the effectiveness of the ensemble-based system with several different aggregation approaches by comparing the results with single classifier-based models. The prediction performance of the AptaCDSS-E system was assessed with a cross-validation test. The experimental results show that our system achieves high diagnosis accuracy (94%) and comparably small prediction difference intervals (