With the progression of an aging society, the number of patients with gait disorders is increasing. Representative conditions causing such disorders include lumbar spinal canal stenosis and cervical spondylotic myelopathy. This study aims to estimate these diseases from gait videos recorded from a lateral view of the patient. However, capturing gait videos typically requires a large space, and when recorded in a limited environment, the videos may suffer from occlusion and distortion, potentially degrading estimation accuracy. To address this issue, we propose a robust disease estimation method using Deep Mutual Learning (DML), where multiple models learn while sharing knowledge with each other. In the proposed framework, two deep learning models with shared parameters are used: one receives complete gait data and the other receives incomplete gait data. The models are trained to extract similar features from both types of input, enabling reliable disease estimation even from degraded videos. We demonstrate the effectiveness of the proposed method through evaluation experiments using gait videos of patients with gait disorders and healthy individuals.