Comprehensive Detection and Analysis of Depression in Women Through Advanced Machine Learning Techniques

Global Health Data estimates that 450 million individuals throughout the globe deal with some kind of mental illness. Also, from their present position of fourth place, depressive disorders are projected to move to the position of most frequent ailment globally by 2020. A frequent and grave mental condition, depression causes a person to feel gloomy and apathetic all the time, making it hard for them to focus on their everyday tasks like work or school. Nearly half of all attempts to commit suicide are influenced by depression, which affects one in fifteen individuals annually. Roughly 30% of persons may experience anxiety throughout their lifetimes; it's a serious mental illness. We provide a method for diagnosing anxiety and depression, two of the most prevalent mental health issues. Machine learning algorithms provide its basis. It would be helpful to know the severity of the patient's condition. Talking to psychologists at different hospitals in India made it quite evident that when first diagnosing common mental problems, the majority of Indian psychologists utilize a standard scale. Bharath Multispecialty Hospital's psychology department gathered anxiety and depression standard scale questionnaires from urban patients (aged 18 to 35). Before the five machine learning approaches were applied to this data, they were pre-processed. When applied to datasets for anxiety and depression, our proposed model has shown five distinct AI strategies to be effective: Our CNN-based model outperforms the competition across a range of metrics, including accuracy, recall, and precision. With a 96.41% accuracy rate for anxiety and a 82.25% accuracy rate for depression, it notably surpasses current algorithms. Furthermore, among urban women aged 18–35, 7.4% suffer from severe anxiety and 15.6% from chronic depression, according to our study.