C4.5: programs for machine learning
C4.5: programs for machine learning
Bankruptcy prediction using neural networks
Decision Support Systems - Special issue on neural networks for decision support
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Machine Learning
Machine Learning
Logistic regression and artificial neural network classification models: a methodology review
Journal of Biomedical Informatics
Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models
Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models
Artificial Intelligence in Medicine
Journal of Biomedical Informatics
Single and multiple time-point prediction models in kidney transplant outcomes
Journal of Biomedical Informatics
A machine learning-based approach to prognostic analysis of thoracic transplantations
Artificial Intelligence in Medicine
Advanced Data Mining Techniques
Advanced Data Mining Techniques
Rescheduling of elective patients upon the arrival of emergency patients
Decision Support Systems
Measuring firm performance using financial ratios: A decision tree approach
Expert Systems with Applications: An International Journal
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
The impact of multinationality on firm value: A comparative analysis of machine learning techniques
Decision Support Systems
Hi-index | 0.00 |
Demand for high-quality, affordable healthcare services increasing with the aging population in the US. In order to cope with this situation, decision makers in healthcare (managerial, administrative and/or clinical) need to be increasingly more effective and efficient at what they do. Along with expertise, information and knowledge are the other key sources for better decisions. Data mining techniques are becoming a popular tool for extracting information/knowledge hidden deep into large healthcare databases. In this study, using a large, feature-rich, nationwide inpatient databases along with four popular machine learning techniques, we developed predictive models; and using an information fusion based sensitivity analysis on these models, we explained the surgical outcome of a patient undergoing a coronary artery bypass grafting. In this study, support vector machines produced the best prediction results (87.74%) followed by decision trees and neural networks. Studies like this illustrate the fact that accurate prediction and better understanding of such complex medical interventions can potentially lead to more favorable outcomes and optimal use of limited healthcare resources.