Empowering Women's Mental Health: A Critical Examination Of Depression Detection

Depression in women is a complex and far-reaching mental health issue that transcends age, gender, and cultural boundaries, making it a global concern of paramount significance. It extends well beyond mood fluctuations, encompassing feelings of hopelessness, despair, and the loss of interest in once-enjoyed activities. In addition to its profound impact on emotions and cognition, depression can manifest through various physical symptoms, including sleep disturbances, appetite changes, and chronic pain. Hormonal fluctuations and the stresses of adjusting to motherhood can significantly contribute to its onset. Comprehending these intricate elements is essential for timely identification and efficient intervention. In this paper, an extensive examination is conducted, systematically reviewing the current body of literature regarding the application of machine learning (ML) and deep learning (DL) methodologies in the detection of depression. While numerous studies have been conducted in this domain, it is evident that deep learning models have received comparatively less attention. Our analysis highlights the limited research conducted on deep learning methods for depression detection. Consequently, this review underscores the need for further research in this area to find the potential of DL in addressing the challenges associated with depression detection. DL, with its capacity to learn hierarchical representations of data, offers a robust framework for depression detection, potentially enhancing early diagnosis and intervention. Recognizing and addressing depression in women is paramount due to its potential to disrupt daily functioning, erode relationships, and diminish overall quality of life. Efforts such as raising awareness, reducing stigma, and providing gender-sensitive mental health care are essential steps in promoting the well-being of women.