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%. The paper describes and motivates the used features, discusses differences between healthy, bipolar 2 and unipolar participants and compares several well-known machine learning classifiers on different classification tasks and with different feature sets.
Keywords: Machine learning; actigraphy watch; actometer data; autoregressive model; depresjon dataset; depression classification.