{"title":"确定加权不平衡数据的特征模式:碰撞损伤研究中胸腰椎骨折的特征选择研究","authors":"Paromita Nitu, P. Madiraju, F. Pintar","doi":"10.1109/IRI49571.2020.00028","DOIUrl":null,"url":null,"abstract":"In motor vehicle crash study, spine injury investigation has a greater impact due to the serious physical, mental and financial consequences. Even though spine fracture deteriorates the quality of life significantly, to the best of our knowledge, there is no study that searched for the exhaustive thoracolumbar spine fracture(TL-fx) feature space to discover potential feature pattern in the motivation of illustrating the increasing risk phenomenon as a function of vehicle model year. This study investigates National Automotive Sampling System Crashworthiness (NASS-CDS) database, year 2000 to 2015. Each year, approximately 4000 to 6000(weighted) occupants are diagnosed with one or multiple TL-fx in road crashes. Even though the TL-fx data support is less than 1.6%, a two-fold feature selection model in a combination of random forest and lift measure based Apriori algorithm generates insightful association rules yielding prominent feature patterns and promotes further investigation to build causal model for the TL-fx study.","PeriodicalId":93159,"journal":{"name":"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...","volume":"18 1","pages":"142-147"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Identifying Feature Pattern for Weighted Imbalance Data: A Feature Selection Study for Thoracolumbar Spine Fractures in Crash Injury Research\",\"authors\":\"Paromita Nitu, P. Madiraju, F. Pintar\",\"doi\":\"10.1109/IRI49571.2020.00028\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In motor vehicle crash study, spine injury investigation has a greater impact due to the serious physical, mental and financial consequences. Even though spine fracture deteriorates the quality of life significantly, to the best of our knowledge, there is no study that searched for the exhaustive thoracolumbar spine fracture(TL-fx) feature space to discover potential feature pattern in the motivation of illustrating the increasing risk phenomenon as a function of vehicle model year. This study investigates National Automotive Sampling System Crashworthiness (NASS-CDS) database, year 2000 to 2015. Each year, approximately 4000 to 6000(weighted) occupants are diagnosed with one or multiple TL-fx in road crashes. Even though the TL-fx data support is less than 1.6%, a two-fold feature selection model in a combination of random forest and lift measure based Apriori algorithm generates insightful association rules yielding prominent feature patterns and promotes further investigation to build causal model for the TL-fx study.\",\"PeriodicalId\":93159,\"journal\":{\"name\":\"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...\",\"volume\":\"18 1\",\"pages\":\"142-147\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IRI49571.2020.00028\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IRI49571.2020.00028","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Identifying Feature Pattern for Weighted Imbalance Data: A Feature Selection Study for Thoracolumbar Spine Fractures in Crash Injury Research
In motor vehicle crash study, spine injury investigation has a greater impact due to the serious physical, mental and financial consequences. Even though spine fracture deteriorates the quality of life significantly, to the best of our knowledge, there is no study that searched for the exhaustive thoracolumbar spine fracture(TL-fx) feature space to discover potential feature pattern in the motivation of illustrating the increasing risk phenomenon as a function of vehicle model year. This study investigates National Automotive Sampling System Crashworthiness (NASS-CDS) database, year 2000 to 2015. Each year, approximately 4000 to 6000(weighted) occupants are diagnosed with one or multiple TL-fx in road crashes. Even though the TL-fx data support is less than 1.6%, a two-fold feature selection model in a combination of random forest and lift measure based Apriori algorithm generates insightful association rules yielding prominent feature patterns and promotes further investigation to build causal model for the TL-fx study.