AUTOMATING MENTAL HEALTH ASSESSMENTS: A MACHINE LEARNING APPROCH TO ANXIETY STRESS DEPRESSION PREDICTION
Keywords:
Anxiety Detection, Stress Prediction, Depres- sion Prediction, Machine Learning, Hybrid Models, Random Forest, Support Vector Machine, Ensemble Learning, XG- Boost, AdaBoost, Feature Engineering, Explainable AI (XAI), SHAP, LIME, Cloud Deployment, Healthcare Integration, Mental Health Monitoring, Multi-Modal Data, Psychometric Analysis, Real-Time Monitoring, Personalized InterventionsAbstract
Depression and stress are experienced by nervous individuals all over the world leading to their diminished perfor- mance and psychological health along with their life satisfaction on a daily basis. The diagnostic procedures are more effective nowadays, whereas early mental health diagnostic procedures are not available in non-medical institutions. The analysis of anxiety, stress, and the level of depressions with usage of mixed classic and innovative machine learning models is created into a predictive system. Its predictive algorithms are XGBoost and AdaBoost, including Random Forest technique and Support Vector Machine technique. The diagnostic measure examines complex stylistic relationship with biological facts and measures of observation behavior and the result of the survey of multi- source data reported to a patient. Solutions of XAI, deployed in the researches, include SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations). XAI tools provide healthcare workers along with end-users with information on a straightforward level about how a model makes decisions in regards to mental healthcare tasks, which requirement a high level of integrity in their performance and their behavior. One of the frameworks within this system applies the extent of mental health conditions as a diagnostic to the creation of interventional approaches using its recommendation module. The system is also adaptive in its suggested interventions as it keeps a record of mental health of the user even across a long period of observation. The first one is general accessibility and real-time through deployment to a cloud-based platform. The configuration of the system on a cloud platform tends to ensure that remote users can get access to live status updates as they request urgent assistance via ideal health system connectivity. The users became the fundamental part of the created entire system since the research made it possible to efficiently scale up to superior digital mental health solutions placing users at the forefront to identify and handle their mental health.
