AI based self-care solution for mental health assessment
Machine learning based solution for self-analysis and journaling of mental health, primarily focused for women.
The client is a well-known Ivy league university based from the USA.
According to WHO, approximately 280 million people suffer from depression around the world. Yet, 75% of them are un-diagnosed. Most people around the world do not realize that they’re suffering from mental illness, due to lack of resources/awareness.
Women are twice as likely to suffer from mental illness compared to men. However, most do not seek medical help due to lack of knowledge.
Trained an AI model with qualitative and quantitative data of mental health for analyzing the depression and anxiety levels of the user.
Users can undergo Mood rating on a scale of 1-5, which is stored on a daily basis.
Under Check-in, users can take anxiety and depression self-assessment by answering a series of scientifically approved questions. Based on those answers, the user is given anxiety and depression severity. Based on these learnings, the app suggests the user if she needs a consultation from a medical professional.
Self-assessment data is stored on a continuous basis for the ML model to learn.
If the anxiety and depression levels are high, the user can contact the crisis support helpline number or find more help under the ‘Resources’ section.
To understand the mental health progress mathematically and visually, users can view the graphs of various assessments done.
Users can put up daily task lists and reminders under the ‘Self-care’ section.
Early diagnosis of mental health issues and appropriate treatment can be directed under the guidance of a medical professional.
31% of female users were able to cope up with mental health issues during their menstrual cycles and showed signs of improved happiness and overall well-being.