@article{Ding_Schmitt_Kryscio_Charnigo_2021, title={Comparison of neural network and logistic regression for dementia prediction: results from the PREADViSE trial}, volume={69}, url={http://www.jgerontology-geriatrics.com/article/view/311}, DOI={10.36150/2499-6564-N311}, abstractNote={<p>Objective. Two systematic reviews suggest that current parametric predictive models are not recommended for use in population demen- tia diagnostic screening. This study was to compare predictive perfor- mance between logistic regression (conventional method) and neural network (non-conventional method).<br>Method. Neural network analysis was performed through the R pack- age “Neuralnet” by using the same covariates as the logistic regression model. Results. Results show that neural network had a slightly ap- parently better predictive performance (area under curve (AUC): 0.732 neural network vs. 0.725 logistic regression). Neural network performed similarly as logistic regression. Furthermore, logistic regression con- firmed that the interaction effect among covariates, which elucidated from neural network.<br>Conclusions. Neural network performed slightly apparently better than logistic regression, and it is able to elucidate complicated relationships among covariates."</p>}, number={2}, journal={JOURNAL OF GERONTOLOGY AND GERIATRICS}, author={Ding, Xiuhua and Schmitt, Frederick and Kryscio, Richard and Charnigo, Richard}, year={2021}, month={Mar.}, pages={137-146} }