多特征融合的抑郁倾向识别方法

Identification method of depressive tendency with multiple feature fusion

近些年,抑郁倾向趋于年轻化和常态化,虽然相关研究已取得一定成果,但仍缺乏更为客观、准确的抑郁倾向识别方法,也缺乏从不同角度研究抑郁倾向,因此,提出将心理健康自查表和眼动追踪结合作为识别抑郁倾向的方法,并且创新地从多角度对抑郁倾向进行研究,即将眼动特征、记忆力特征、认知风格特征以及网络行为特征多种类型特征融合。为了处理复杂的特征关系,提出扫描过程来处理复杂的特征关系,并将扫描过程与堆叠法结合提出抑郁倾向识别模型——扫描堆叠模型。为了全面客观评价扫描堆叠模型的性能,对扫描过程和堆叠法的独立贡献进行了实验。实验结果显示扫描过程独立贡献为0. 03,堆叠法独立贡献为0. 02,并且扫描堆叠模型与多种模型从参数R平方、均方误差、平均绝对误差进行比较,结果为扫描堆叠模型的预测效果较好。

In recent years, the tendency of depression tends to occur at a younger age and affects more people. Although research on the topic has achieved some results, it still lacks a more objective and accurate method for identifying depressive tendencies, and research on depressive tendencies from multiple perspectives is lacking. Therefore, the combination of mental health self-check table and eye-tracking was proposed as a method for identifying depressive tendencies and was studied from multiple perspectives. The innovative features of eye movement, memory, cognitive style, and network behaviors were incorporated. In order to address complex feature relationship and extract more useful information, a scanning process with combining a stacking method was proposed to form a proposed recognition model for depressive tendencies called scanning stacking model. To comprehensively and objectively evaluate the performance of scanning and stacking model, the independent contributions of both scanning process and stacking method were evaluated in the experiment. The experimental results show that the independent contribution of scanning process is 0. 03, and the independent contribution of stacking method is 0. 02. In addition, the scanning stacking model was compared with several models from parameter R-squared, Mean Square Error( MSE) and average absolute error, and the results show that the scanning stacking model has better prediction effect.