Download Project Document/Synopsis
Depression is a leading cause of mental illness, and it has been linked to an increased risk of premature death. Furthermore, it is a major contributor to suicidal thoughts and causes significant impairment in daily life. Every year, one in every 15 adults suffers from depression, affecting 300 million people worldwide. Several previous empirical studies have shown that certain linguistic characteristics can be analyzed and correlated to likely depression symptoms, as well as help predict self-destructive behavior.
This Depression Detection System detects the type of depression (anxiety, PTSD, or bipolar) and recommends nearby clinics to consult a psychiatrist. Furthermore, the user must speak for 1 minute about themselves while their facial expressions are being recorded. The user must take a quiz and answer all of the questions.
In this system, Naive Bayes is used for the quiz. CNN and a unique dataset for Depression faces will be created for Image & Video. This system detects the type of depression and recommends that nearby clinics consult a psychiatrist.
The programming language used in this system is Python. Html, CSS, and JavaScript are used on the front end, and MySQL is used on the back end. Here, we have used the Django framework.
Humans have a stronger sense of emotions and feelings, which can be combined with technology to create useful tools. This depression detection system is extremely useful in facilitating depression self-evaluation and improving diagnostic accuracy. In addition, for 1 minute, the user must say something about themselves, and their facial emotion will be detected.
Advantages
- The user can take self-tests with quizzes to know what type of depression they are suffering from.
- Easy to find nearby clinics to consult a psychiatrist.