{"title":"树集成方法和包裹识别与阿尔茨海默病相关的大脑区域","authors":"M. Wehenkel, C. Bastin, C. Phillips, P. Geurts","doi":"10.1109/PRNI.2017.7981513","DOIUrl":null,"url":null,"abstract":"For several years, machine learning approaches have been increasingly investigated in the neuroimaging field to help the diagnosis of dementia. To this end, this work proposes a new pattern recognition technique based on brain parcelling, group selection and tree ensemble algorithms. In addition to prediction performance competitive with more traditional approaches, the method provides easy interpretation about the brain regions involved in the prognosis of Alzheimer’s disease.","PeriodicalId":429199,"journal":{"name":"2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Tree ensemble methods and parcelling to identify brain areas related to Alzheimer’s disease\",\"authors\":\"M. Wehenkel, C. Bastin, C. Phillips, P. Geurts\",\"doi\":\"10.1109/PRNI.2017.7981513\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For several years, machine learning approaches have been increasingly investigated in the neuroimaging field to help the diagnosis of dementia. To this end, this work proposes a new pattern recognition technique based on brain parcelling, group selection and tree ensemble algorithms. In addition to prediction performance competitive with more traditional approaches, the method provides easy interpretation about the brain regions involved in the prognosis of Alzheimer’s disease.\",\"PeriodicalId\":429199,\"journal\":{\"name\":\"2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PRNI.2017.7981513\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PRNI.2017.7981513","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Tree ensemble methods and parcelling to identify brain areas related to Alzheimer’s disease
For several years, machine learning approaches have been increasingly investigated in the neuroimaging field to help the diagnosis of dementia. To this end, this work proposes a new pattern recognition technique based on brain parcelling, group selection and tree ensemble algorithms. In addition to prediction performance competitive with more traditional approaches, the method provides easy interpretation about the brain regions involved in the prognosis of Alzheimer’s disease.