Introduced by Jillian Chan, Yuyang Pan, Simarjeev Singh and Yin Zhang

The Anxiety and Depression Association of America estimates that 16.1 million people in the United States are impacted by major depression. Yet, depression is severely underdiagnosed and only a fraction of the people affected by it will ever receive treatment.

But now, a team of UC Berkeley students are using machine learning to to help predict a person’s likelihood of experiencing depression, making diagnosis easier. Using 12 years of data from national health organization Geisinger, the students created a model that uses personal information about a user to predict the likelihood that they could experience depression.

In particular, the students’ project analyzes the correlation between anxiety, insomnia and depression while also taking into account demographic factors, such as age, gender and ethnicity.

Through their investigation they verified that the relationships among depression, insomnia and anxiety are multi-directional, meaning each of these condition could trigger or worsen one of the other two conditions. Furthermore, the students confirmed previous research that concluded that females, anxiety patients and insomnia patients are at a greater risk of developing depression.

The project gives a general sense of which populations are more likely to be diagnosed as depressed and will eventually help doctors identify a patient’s likelihood of experiencing depression.

The team is now working to increase the accuracy of the classical models used for the project, which is currently around 70 percent. In order to improve accuracy, the students hope to integrate patient histories of drug use, alcohol consumption and smoking into the factors taken into account by their model.