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Currently, the clinicians are unable to identify with complete confidence which intervention is best for a patient and the referral is often made in trial and error fashion. Only 58% recommendations are successful in our dataset. We aim to develop an interpretable decision support system using machine learning to assist the clinicians. We proposed an alternate ripper (ARIPPER) combined with a hybrid re-sampling technique, and a balanced weighted random forests (BWRF) ensemble method respectively, in order to tackle the multi-class imbalance, class overlap and noise problem in real world application data. The final models have shown promising potential in classification compared to human baseline and has been integrated into a web-based decision-support tool that requires additional validation in a clinical sample.<\/jats:p>","DOI":"10.1186\/2047-2501-1-15","type":"journal-article","created":{"date-parts":[[2013,12,4]],"date-time":"2013-12-04T14:26:21Z","timestamp":1386167181000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["On the application of multi-class classification in physical therapy recommendation"],"prefix":"10.1007","volume":"1","author":[{"given":"Jing","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Peng","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Douglas P","family":"Gross","sequence":"additional","affiliation":[]},{"given":"Osmar R","family":"Zaiane","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2013,12,4]]},"reference":[{"key":"19_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1007730.1007733","volume":"6","author":"NV Chawla","year":"2004","unstructured":"Chawla NV, Japkowicz N, Kolcz A: Editorial: special issue on learning from imbalanced data sets. 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