{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T03:04:53Z","timestamp":1765422293340,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"print","value":"9781643683027"},{"type":"electronic","value":"9781643683034"}],"license":[{"start":{"date-parts":[[2022,8,17]],"date-time":"2022-08-17T00:00:00Z","timestamp":1660694400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,8,17]]},"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3233\/shti220800","type":"book-chapter","created":{"date-parts":[[2022,8,17]],"date-time":"2022-08-17T19:25:34Z","timestamp":1660764334000},"source":"Crossref","is-referenced-by-count":4,"title":["Machine Learning Based Classification of Depression Using Motor Activity Data and Autoregressive Model"],"prefix":"10.3233","author":[{"given":"Alexander","family":"Schulte","sequence":"first","affiliation":[{"name":"University of Applied Sciences and Arts Dortmund"}]},{"given":"Tim","family":"Breiksch","sequence":"additional","affiliation":[{"name":"University of Applied Sciences and Arts Dortmund"}]},{"given":"Jonas","family":"Brockmann","sequence":"additional","affiliation":[{"name":"University of Applied Sciences and Arts Dortmund"}]},{"given":"Nadja","family":"Bauer","sequence":"additional","affiliation":[{"name":"University of Applied Sciences and Arts Dortmund"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","German Medical Data Sciences 2022 \u2013 Future Medicine: More Precise, More Integrative, More Sustainable!"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI220800","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,17]],"date-time":"2022-08-17T19:25:55Z","timestamp":1660764355000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI220800"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,17]]},"ISBN":["9781643683027","9781643683034"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti220800","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"type":"print","value":"0926-9630"},{"type":"electronic","value":"1879-8365"}],"subject":[],"published":{"date-parts":[[2022,8,17]]}}}