AI-Driven Depression Level Predection for Enhanced Mental Health Diagnostics

Abstract

AI is significantly impacting the way that mental health diagnostic tools are developed through their ability to provide affordable, accessible and efficient means of detecting psychological disorders like depression. While the current state of screening includes many effective tools (i.e., Clinical Interviews and Self- Reported Questionnaires) they have some inherent shortcomings; these include, but are not limited to, being subjective and/or delayed diagnoses, lack of access to individuals who may be experiencing difficulties with their mental health, and dependency on the availability of professional interventions. The shortfalls identified above demonstrate the need for the development of intelligent systems, capable of conducting rapid and accurate evaluations of an individualʼs mental health. An AI-driven Depression Level Prediction System was therefore created to collect structured clinical information, as well as unstructured textual input in order to create a full and complete assessment of an individualʼs mental health condition. Utilizing the PHQ 9 survey instrument as the basis for collecting clinical information, the system utilizes Natural Language Processing techniques to evaluate user-generated text, thereby gaining further insight into an individualʼs emotional and psychological trends. The system described herein utilizes three machine learning-based predictive models Random Forest, SVM, XGBoost) to predict an individualʼs level of depression as one of four categories (minimal, mild, moderate or severe). Unlike prior binary prediction models utilized in the context of mental health evaluations, the described model provides fine-tuned evaluations that can be more effectively used in practical applications of mental health monitoring. Additionally, Explainable AI techniques were incorporated into the design of the system to improve transparency and interpretability of the results produced by the system. Such capabilities enable both patients and clinicians to identify specific variables within the results that contributed to the systemʼs predictions. The modular nature of the system enables scalability, flexibility and efficient operation of the system even when utilizing lightweight hardware that does not require extensive computing capabilities. Experimental validation demonstrated that the described system achieved greater accuracy and better generalization than other systems currently available. Through its ability to process both behavioral inputs, questionnaire responses and textual sentiment analysis, the system offers a holistic view of an individualʼs mental health status. Beyond improving early detection, the described system can assist clinicians and patients in making informed decisions regarding treatment options for issues related to mental health. Therefore, the system serves as a connection between traditional healthcare practices and emerging AI technology to provide a private and secure method for evaluating mental health conditions.