Neuro-Fuzzy classifier for longitudinal behavioral intervention data
Neuro-Fuzzy classifier for longitudinal behavioral intervention data
复制标题
用于纵向行为干预数据的神经模糊分类器
DOI：
10.1109/iccnc.2019.8685574
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Honggang Wang
中科院分区：
文献类型：
--
作者：
Venkata Sukumar Gurugubelli;Hua Fang;Honggang Wang
基于模糊逻辑的算法已被应用于学习纵向行为干预数据。针对纵向随机对照试验（RCT）数据中的缺失数据，提出了一种改进的广义神经模糊（mGNNF）分类器。具体而言，使用所有可用属性、人口统计学、尼古丁依赖性和干预属性，该拟议分类器用于预测纵向戒烟RCT后的长期戒烟[1]。我们的模型比较研究表明，相同的纵向RCT数据，mGNNF显示出更高的准确性相比，三个类似的模糊逻辑为基础的分类，虽然它的计算时间比这些比较慢。
Fuzzy-logic based algorithms have been applied in learning longitudinal behavioral intervention data. This paper proposes a modified generalized network-based neuro-fuzzy (mGNNF) classifier for longitudinal randomized controlled trial (RCT) data with missing values. Specifically, using all available attributes, demographic, nicotine dependence, and intervention attributes, this proposed classifier is used to predict the prolonged smoking abstinence after a longitudinal smoking cessation RCT [1]. Our model comparison study shows that with the same longitudinal RCT data, mGNNF shows a higher accuracy compared to three similar fuzzy-logic based classifiers, although its computational time is slower than two of these comparators.