Diagnosing Major Depressive Disorder using Activity Data from Wearable Sensors and Machine Learning - Desai, Shlok; Marsh, Brianna (Journal of Student Research, 2022)
Major Depressive Disorder (MDD), a mood disorder, is the most common psychological disorder. MDD manifests itself through a range of deadly symptoms, while diagnosis remains difficult and costly, often requiring psychiatrists or specialized techniques. An easier, and possibly early, diagnosis could improve treatment and outcomes. To address this unmet need, we developed a novel machine learning algorithm to detect MDD based on an individual's activity data i.e. movement combined with light data. The dataset from Kaggle.com included activity data for fifty-five participants in 2021. Our algorithm determined that disturbances in activity is a symptom that can be used to predict Major Depressive Disorder. This insight has the potential to accurately detect and diagnose a person with MDD. In conclusion, the algorithm connecting activity to MDD paves the way to an easier and effective way of diagnosing depression.