Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/3563

Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Novel logistic regression models to aid the diagnosis of dementia
Authors: Mazzocco, Thomas
Hussain, Amir
Contact Email: ahu@cs.stir.ac.uk
Keywords: Dementia
Diagnosis
Variables selection
Logistic regression
Prediction model
Decision support system
Issue Date: Feb-2012
Publisher: Elsevier
Citation: Mazzocco T & Hussain A (2012) Novel logistic regression models to aid the diagnosis of dementia, Expert Systems with Applications, 39 (3), pp. 3356-3361.
Abstract: Clinicians often experience difficulties in the diagnosis of dementia due to the intrinsic complexity of the process and lack of comprehensive diagnostic tools. Different models have been proposed to provide medical decision support in dementia diagnosis. The aim of this study is to improve on the performance of a recent application of Bayesian belief networks using an alternative approach based on logistic regression. A pool of 14 variables has been evaluated in a sample of 164 patients suspected of dementia. First, a logistic regression model for dementia prediction is developed using all variables included in the previous model; then, a second model is built using a stepwise logistic regression starting with all collected variables and selecting the pool of the relevant ones. A range of performance metrics have been used to evaluate the developed models. The new models have resulted in very good predictive power, demonstrating general performance improvement compared to a state-of-the-art prediction model. Interestingly, the approach based on statistical variables selection outperformed the model which used variables selected by domain experts in the previous study. Further collaborative studies are now required to determine the optimal approach and to overcome existing limitations imposed by the size of the considered sample.
Type: Journal Article
URI: http://hdl.handle.net/1893/3563
DOI Link: http://dx.doi.org/10.1016/j.eswa.2011.09.023
Rights: The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author; you can only request a copy if you wish to use this work for your own research or private study.
Affiliation: University of Stirling
Computing Science - CSM Dept

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