SVM Based Predictive Model for SGA Detection

被引：0

作者：

Mo, Haowen ^{[1
,2
]}

Li, Jianqiang ^{[1
,2
]}

Chen, Shi ^{[3
]}

Pan, Hui ^{[3
]}

Yang, Ji-Jiang ^{[4
]}

Wang, Qing ^{[4
]}

Mao, Rui ^{[2
]}

机构：

[1] Beijing Univ Technol, Sch Software Engn, Beijing, Peoples R China

[2] Shenzhen Key Lab Serv Comp & Applicat, Guangdong Key Lab Popular High Performance Comp, Shenzhen, Peoples R China

[3] Beijing Union Med Coll Hosp, Dept Endocrinol, Beijing, Peoples R China

[4] Tsinghua Univ, Tsinghua Natl Lab Informat Sci & Technol, Beijing, Peoples R China

来源：

INCLUSIVE SMART CITIES AND DIGITAL HEALTH | 2016年 / 9677卷

关键词：

Small for gestational age; Support vector machine; Classification; Healthcare; GESTATIONAL-AGE; CLASSIFICATION;

D O I：

10.1007/978-3-319-39601-9_6

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The medical diagnosis process can be interpreted as a decision making process, which doctors determine whether a person is suffering from a disease based on the medical examination. This process can also be computerized in order to present medical diagnostic procedures in an accurate, objective, rational, and fast way. This paper presents a detection model for small for gestational age (SGA) based on support vector machine (SVM). For this purpose, a dataset was adopted from pregnancy eugenic investigation to train the classification model. Then empirical experiments were conducted for SGA detection. The results indicate that support vector machine is considerably effective to detect SGA to help doctors make the final diagnosis.

引用

页码：59 / 68

页数：10

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