Multimodal Depression Severity Prediction from medical bio-markers using Machine Learning Tools and Technologies

TL;DR

This paper presents a multimodal machine learning approach using a custom ensemble model to predict depression severity through language, audio, and visual cues collected via a smartphone app, achieving high accuracy and precision.

Contribution

It introduces a novel cross-platform app and a custom ensemble method for multimodal depression severity prediction, addressing dataset limitations and feature relevance.

Findings

Achieved 91.56% accuracy in depression classification

Precision of 0.88 in depression severity prediction

Optimized feature selection enhances model performance

Abstract

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.