  Depression has been a leading cause of mental-health illnesses across the
world. While the loss of lives due to unmanaged depression is a subject of
attention, so is the lack of diagnostic tests and subjectivity involved. Using
behavioural cues to automate depression diagnosis and stage prediction in
recent years has relatively increased. However, the absence of labelled
behavioural datasets and a vast amount of possible variations prove to be a
major challenge in accomplishing the task. This paper proposes a novel Custom
CM Ensemble approach and focuses on a paradigm of a cross-platform smartphone
application that takes multimodal inputs from a user through a series of
pre-defined questions, sends it to the Cloud ML architecture and conveys back a
depression quotient, representative of its severity. Our app estimates the
severity of depression based on a multi-class classification model by utilizing
the language, audio, and visual modalities. The given approach attempts to
detect, emphasize, and classify the features of a depressed person based on the
low-level descriptors for verbal and visual features, and context of the
language features when prompted with a question. The model achieved a precision
value of 0.88 and an accuracy of 91.56%. Further optimization reveals the
intramodality and intermodality relevance through the selection of the most
influential features within each modality for decision making.

Artificial Intelligence;Machine Learning