Novel logistic regression models to aid the diagnosis of dementia
Novel logistic regression models to aid the diagnosis of dementia
复制标题
DOI：
10.1016/j.eswa.2011.09.023
复制
发表时间：
2012-02-15
影响因子：
8.5
通讯作者：
Hussain, Amir
中科院分区：
文献类型：
--
作者：
Mazzocco, Thomas;Hussain, Amir
由于痴呆症诊断过程的内在复杂性和缺乏全面的诊断工具，临床医生经常遇到困难。已经提出了不同的模型来提供痴呆症诊断中的医疗决策支持。本研究的目的是使用一种基于逻辑回归的替代方法来改进贝叶斯信念网络的性能，在164例疑似痴呆患者的样本中评估了14个变量。首先，使用先前模型中包含的所有变量开发痴呆预测的逻辑回归模型;然后，使用逐步逻辑回归从所有收集的变量开始并选择相关变量的池来构建第二个模型。一系列的性能指标已被用来评估开发的模型。新的模型已经导致了非常好的预测能力，表现出一般的性能改善相比，一个国家的最先进的预测模型。有趣的是，基于统计变量选择的方法优于使用领域专家在以前的研究中选择的变量的模型。现在需要进行进一步的合作研究，以确定最佳办法，并克服所考虑的样本规模所造成的现有限制。(C)2011爱思唯尔有限公司版权所有。
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. (C) 2011 Elsevier Ltd. All rights reserved.