Machine Learning Based Classification of Depression Using Motor Activity Data and Autoregressive Model.

Machine learning based disease classification have already achieved amazing results in medicine: for example, models can find a tumor in computer tomography images at least as accurately as experts in the field. Since the development and widespread use of actigraphy watches, activity data has been used as a basis for diagnosing various diseases such as depression or Alzheimer's disease. In this study, we use a dataset with activity measurements of mentally ill and healthy people, calculate various features and achieve a classification accuracy of over 78%.