Pelvic Injury Discriminative Model Based on Data Mining Algorithm.
10.12116/j.issn.1004-5619.2020.201009
- Author:
Fei-Xiang WANG
1
;
Rui JI
2
;
Lu-Ming ZHANG
3
;
Peng WANG
3
;
Tai-Ang LIU
3
;
Lu-Jie SONG
4
;
Mao-Wen WANG
1
;
Zhi-Lu ZHOU
1
;
Hong-Xia HAO
1
;
Wen-Tao XIA
1
Author Information
1. 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.
2. Reproductive Medical Center, People's Hospital of Wuhan University, Wuhan 430072, China.
3. Qidong Yingwei Information Technology Co., Ltd., Qidong 226200, Jiangsu Province, China.
4. The Sixth People's Hospital Affiliated to Shanghai Jiaotong University, Shanghai 200233, China.
- Publication Type:Journal Article
- Keywords:
computed tomography;
forensic medicine;
partial least squares;
pelvis;
principal component analysis;
support vector machine
- MeSH:
Algorithms;
Bayes Theorem;
Data Mining;
Least-Squares Analysis;
Support Vector Machine
- From:
Journal of Forensic Medicine
2022;38(3):350-354
- CountryChina
- Language:English
-
Abstract:
OBJECTIVES:To 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.
METHODS:Eighty 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.
RESULTS:The 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.
CONCLUSIONS:In 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.
