A Comparative approach of Depression Detection: A state of Art

Depression is an unconscious state of mind; it directly affects the mental health condition of patients, so it is required to accurately detect with a certain time to prevent severe depression. Depression detection with traditional approaches includes the patient's interviews and self-reporting to doctors or psychotherapists, but this approach faces challenges like time consumption and subjectivity. Another approach is to detect depression by using some computational techniques, including ‘machine learning (ML)’, ‘deep learning’, ‘natural language processing’, and also some wearable technologies or devices. The study compares the hybrid ensemble model and the late fusion method with other classifiers that are used for depression detection based on various performance parameters, like ‘accuracy’, classification report, ‘mean squared error’, and R2 score , etc. This study also discusses some ethical terms, such as the privacy of patient data and the risk of misdiagnosis. The study highlights the multimodal approach of various modality to build an effective and personalized system for depression detection.