  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.
