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Few polyQ disease studies have evaluated AAO predicted by machine\u2010learning algorithms and linear regression methods.<\/jats:p><\/jats:sec><jats:sec><jats:title>Objective<\/jats:title><jats:p>The objective of this study was to develop a machine\u2010learning model for AAO prediction in the largest spinocerebellar ataxia type 3\/Machado\u2013Joseph disease (SCA3\/MJD) population from mainland China.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In this observational study, we introduced an innovative approach by systematically comparing the performance of 7 machine\u2010learning algorithms with linear regression to explore AAO prediction in SCA3\/MJD using CAG expansions of 10 polyQ\u2010related genes, sex, and parental origin.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Similar prediction performance of testing set and training set in each models were identified and few overfitting of training data was observed. Overall, the machine\u2010learning\u2010based XGBoost model exhibited the most favorable performance in AAO prediction over the traditional linear regression method and other 6 machine\u2010learning algorithms for the training set and testing set. The optimal XGBoost model achieved mean absolute error, root mean square error, and median absolute error of 5.56, 7.13, 4.15\u2009years, respectively, in testing set 1, with mean absolute error (4.78\u2009years), root mean square error (6.31\u2009years), and median absolute error (3.59\u2009years) in testing set 2.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Machine\u2010learning algorithms can be used to predict AAO in patients with SCA3\/MJD. The optimal XGBoost algorithm can provide a good reference for the establishment and optimization of prediction models for SCA3\/MJD or other polyQ diseases. \u00a9 2020 International Parkinson and Movement Disorder Society<\/jats:p><\/jats:sec>","DOI":"10.1002\/mds.28311","type":"journal-article","created":{"date-parts":[[2020,9,29]],"date-time":"2020-09-29T17:38:17Z","timestamp":1601401097000},"page":"216-224","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Prediction of the Age at Onset of Spinocerebellar Ataxia Type 3 with Machine Learning"],"prefix":"10.1002","volume":"36","author":[{"given":"Linliu","family":"Peng","sequence":"first","affiliation":[{"name":"Department of Neurology Xiangya Hospital, Central South University  Changsha China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhao","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Neurology Xiangya 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