论文标题
具有不同初始化方法检测的神经网络
Neural Networks with Different Initialization Methods for Depression Detection
论文作者
论文摘要
作为一种常见的精神障碍，抑郁症是全球各种疾病的主要原因。抑郁症的早期发现和治疗可以极大地促进缓解并防止复发。但是，常规的抑郁诊断方式需要大量的人为努力并导致经济负担，同时仍然容易误诊。另一方面，最近的研究报告说，身体特征是抑郁症诊断的主要因素，这激发了我们通过神经网络挖掘内部关系，而不是依靠临床经验。在本文中，构建了神经网络以预测物理特征的抑郁症。检查了两种初始化方法 - Xaiver和Kaiming初始化。实验结果表明，具有凯明初始化的三层神经网络可实现$ 83 \％$的精度。
As a common mental disorder, depression is a leading cause of various diseases worldwide. Early detection and treatment of depression can dramatically promote remission and prevent relapse. However, conventional ways of depression diagnosis require considerable human effort and cause economic burden, while still being prone to misdiagnosis. On the other hand, recent studies report that physical characteristics are major contributors to the diagnosis of depression, which inspires us to mine the internal relationship by neural networks instead of relying on clinical experiences. In this paper, neural networks are constructed to predict depression from physical characteristics. Two initialization methods are examined - Xaiver and Kaiming initialization. Experimental results show that a 3-layers neural network with Kaiming initialization achieves $83\%$ accuracy.
