Name
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.eswa.2011.09.023 | Expert Syst. Appl. |
Keywords | Field | DocType |
novel logistic regression model,dementia diagnosis,different model,developed model,previous model,new model,alternative approach,dementia prediction,statistical variables selection,state-of-the-art prediction model,logistic regression model,prediction model,logistic regression,decision support system,diagnosis | Data mining,Stepwise regression,Predictive power,Regression diagnostic,Computer science,Decision support system,Bayesian network,Artificial intelligence,Logistic regression,Machine learning,Dementia,Performance improvement | Journal |
Volume | Issue | ISSN |
39 | 3 | 0957-4174 |
Citations | PageRank | References |
9 | 0.85 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thomas Mazzocco | 1 | 42 | 3.87 |
| Amir Hussain | 2 | 705 | 29.16 |