C4.5: programs for machine learning
C4.5: programs for machine learning
The process of knowledge discovery in databases
Advances in knowledge discovery and data mining
Expert Systems with Applications: An International Journal
Analysis of healthcare coverage: A data mining approach
Expert Systems with Applications: An International Journal
Evaluating of traumatic brain injuries using artificial neural networks
Expert Systems with Applications: An International Journal
An analytic approach to select data mining for business decision
Expert Systems with Applications: An International Journal
A predictive model for cerebrovascular disease using data mining
Expert Systems with Applications: An International Journal
WBCD breast cancer database classification applying artificial metaplasticity neural network
Expert Systems with Applications: An International Journal
Review: A review of data mining applications for quality improvement in manufacturing industry
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
An intelligent model for the classification of children's occupational therapy problems
Expert Systems with Applications: An International Journal
A general regression neural network
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Proposing a Business Model in Healthcare Industry: E-Diagnosis
International Journal of Healthcare Information Systems and Informatics
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Acquired brain injury (ABI) is one of the leading causes of death and disability in the world and is associated with high health care costs as a result of the acute treatment and long term rehabilitation involved. Different algorithms and methods have been proposed to predict the effectiveness of rehabilitation programs. In general, research has focused on predicting the overall improvement of patients with ABI. The purpose of this study is the novel application of data mining (DM) techniques to predict the outcomes of cognitive rehabilitation in patients with ABI. We generate three predictive models that allow us to obtain new knowledge to evaluate and improve the effectiveness of the cognitive rehabilitation process. Decision tree (DT), multilayer perceptron (MLP) and general regression neural network (GRNN) have been used to construct the prediction models. 10-fold cross validation was carried out in order to test the algorithms, using the Institut Guttmann Neurorehabilitation Hospital (IG) patients database. Performance of the models was tested through specificity, sensitivity and accuracy analysis and confusion matrix analysis. The experimental results obtained by DT are clearly superior with a prediction average accuracy of 90.38%, while MLP and GRRN obtained a 78.7% and 75.96%, respectively. This study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients.