Machine learning based solution for self-analysis and journaling of mental health, primarily focused for women.

Client Profile

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.
App interface for AI-based self-care solution: Empowering users to assess and prioritize mental health for personalized well-being


  • 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.

Key Benefits

  • 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.

Used technologies

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