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The use of automated procedures can make dysgraphia testing available to larger populations, thereby facilitating early intervention for those who need it. In this paper, we employed a machine learning approach to identify handwriting deteriorated by dysgraphia. To achieve this goal, we collected a new handwriting dataset consisting of several handwriting tasks and extracted a broad range of features to capture different aspects of handwriting. These were fed to a machine learning algorithm to predict whether handwriting is affected by dysgraphia. We compared several machine learning algorithms and discovered that the best results were achieved by the adaptive boosting (AdaBoost) algorithm. The results show that machine learning can be used to detect dysgraphia with almost 80% accuracy, even when dealing with a heterogeneous set of subjects differing in age, sex and handedness.<\/jats:p>","DOI":"10.1038\/s41598-020-78611-9","type":"journal-article","created":{"date-parts":[[2020,12,9]],"date-time":"2020-12-09T06:52:15Z","timestamp":1607496735000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":107,"title":["Dysgraphia detection through machine learning"],"prefix":"10.1038","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6634-4696","authenticated-orcid":false,"given":"Peter","family":"Drot\u00e1r","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marek","family":"Dobe\u0161","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,12,9]]},"reference":[{"key":"78611_CR1","doi-asserted-by":"crossref","unstructured":"Engel-Yeger, B., Nagauker-Yanuv, L. & Rosenblum, S. 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