Pelvic Injury Discriminative Model Based on Data Mining Algorithm
WANG Fei-xiang,
JI Rui,
ZHANG Lu-ming,
WANG Peng,
LIU Tai-ang,
SONG Lu-jie,
WANG Mao-wen,
ZHOU Zhi-lu,
HAO Hong-xia,
XIA Wen-tao
Affiliations
WANG Fei-xiang
Shanghai Key Laboratory of Forensic Medicine， Key Laboratory of Forensic Science， Ministry of Justice， Shanghai Forensic Service Platform， Academy of Forensic Science， Shanghai 200063， China
JI Rui
Reproductive Medical Center， People’s Hospital of Wuhan University，Wuhan 430072， China
ZHANG Lu-ming
Qidong Yingwei Information Technology Co.， Ltd.， Qidong 226200， Jiangsu Province， China
WANG Peng
Qidong Yingwei Information Technology Co.， Ltd.， Qidong 226200， Jiangsu Province， China
LIU Tai-ang
Qidong Yingwei Information Technology Co.， Ltd.， Qidong 226200， Jiangsu Province， China
SONG Lu-jie
The Sixth People’s Hospital Affiliated to Shanghai Jiaotong University， Shanghai 200233， China
WANG Mao-wen
Shanghai Key Laboratory of Forensic Medicine， Key Laboratory of Forensic Science， Ministry of Justice， Shanghai Forensic Service Platform， Academy of Forensic Science， Shanghai 200063， China
ZHOU Zhi-lu
Shanghai Key Laboratory of Forensic Medicine， Key Laboratory of Forensic Science， Ministry of Justice， Shanghai Forensic Service Platform， Academy of Forensic Science， Shanghai 200063， China
HAO Hong-xia
Shanghai Key Laboratory of Forensic Medicine， Key Laboratory of Forensic Science， Ministry of Justice， Shanghai Forensic Service Platform， Academy of Forensic Science， Shanghai 200063， China
XIA Wen-tao
Shanghai Key Laboratory of Forensic Medicine， Key Laboratory of Forensic Science， Ministry of Justice， Shanghai Forensic Service Platform， Academy of Forensic Science， Shanghai 200063， China
ObjectiveTo reduce the dimension of characteristic information extracted from pelvic CT images by using principal component analysis （PCA） and partial least squares （PLS） methods. To establish a support vector machine （SVM） classification and identification model to identify if there is pelvic injury by the reduced dimension data and evaluate the feasibility of its application.MethodsEighty percent of 146 normal and injured pelvic CT images were randomly selected as training set for model fitting， and the remaining 20% was used as testing set to verify the accuracy of the test， respectively. Through CT image input， preprocessing， feature extraction， feature information dimension reduction， feature selection， parameter selection， model establishment and model comparison， a discriminative model of pelvic injury was established.ResultsThe PLS dimension reduction method was better than the PCA method and the SVM model was better than the naive Bayesian classifier （NBC） model. The accuracy of the modeling set， leave-one-out cross validation and testing set of the SVM classification model based on 12 PLS factors was 100%， 100% and 93.33%， respectively.ConclusionIn the evaluation of pelvic injury， the pelvic injury data mining model based on CT images reaches high accuracy， which lays a foundation for automatic and rapid identification of pelvic injuries.
