Evaluating the Risk of Metabolic Syndrome Based on an Artificial Intelligence Model
Metabolic syndrome is worldwide public health problem and is a serious threat to people's health and lives. Understanding the relationship between metabolic syndrome and the physical symptoms is a difficult and challenging task, and few studies have been performed in this field. It is important...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
|
| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2014/207268 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832567507963084800 |
|---|---|
| author | Hui Chen Shenghua Xiong Xuan Ren |
| author_facet | Hui Chen Shenghua Xiong Xuan Ren |
| author_sort | Hui Chen |
| collection | DOAJ |
| description | Metabolic syndrome is worldwide public health problem and is a serious threat to people's health and lives. Understanding the relationship between metabolic syndrome and the physical symptoms is a difficult and challenging task, and few studies have been performed in this field. It is important to classify adults who are at high risk of metabolic syndrome without having to use a biochemical index and, likewise, it is important to develop technology that has a high economic rate of return to simplify the complexity of this detection. In this paper, an artificial intelligence model was developed to identify adults at risk of metabolic syndrome based on physical signs; this artificial intelligence model achieved more powerful capacity for classification compared to the PCLR (principal component logistic regression) model. A case study was performed based on the physical signs data, without using a biochemical index, that was collected from the staff of Lanzhou Grid Company in Gansu province of China. The results show that the developed artificial intelligence model is an effective classification system for identifying individuals at high risk of metabolic syndrome. |
| format | Article |
| id | doaj-art-42fda6b24a1046afa62df143bc539c32 |
| institution | Kabale University |
| issn | 1085-3375 1687-0409 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Abstract and Applied Analysis |
| spelling | doaj-art-42fda6b24a1046afa62df143bc539c322025-02-03T01:01:18ZengWileyAbstract and Applied Analysis1085-33751687-04092014-01-01201410.1155/2014/207268207268Evaluating the Risk of Metabolic Syndrome Based on an Artificial Intelligence ModelHui Chen0Shenghua Xiong1Xuan Ren2Department of Endocrinology, The Second Hospital of Lanzhou University, Lanzhou 730030, ChinaSchool of Mathematics and Statistics, Lanzhou University, Tianshui Road 222, Lanzhou, Gansu 730000, ChinaDepartment of Endocrinology, The Second Hospital of Lanzhou University, Lanzhou 730030, ChinaMetabolic syndrome is worldwide public health problem and is a serious threat to people's health and lives. Understanding the relationship between metabolic syndrome and the physical symptoms is a difficult and challenging task, and few studies have been performed in this field. It is important to classify adults who are at high risk of metabolic syndrome without having to use a biochemical index and, likewise, it is important to develop technology that has a high economic rate of return to simplify the complexity of this detection. In this paper, an artificial intelligence model was developed to identify adults at risk of metabolic syndrome based on physical signs; this artificial intelligence model achieved more powerful capacity for classification compared to the PCLR (principal component logistic regression) model. A case study was performed based on the physical signs data, without using a biochemical index, that was collected from the staff of Lanzhou Grid Company in Gansu province of China. The results show that the developed artificial intelligence model is an effective classification system for identifying individuals at high risk of metabolic syndrome.http://dx.doi.org/10.1155/2014/207268 |
| spellingShingle | Hui Chen Shenghua Xiong Xuan Ren Evaluating the Risk of Metabolic Syndrome Based on an Artificial Intelligence Model Abstract and Applied Analysis |
| title | Evaluating the Risk of Metabolic Syndrome Based on an Artificial Intelligence Model |
| title_full | Evaluating the Risk of Metabolic Syndrome Based on an Artificial Intelligence Model |
| title_fullStr | Evaluating the Risk of Metabolic Syndrome Based on an Artificial Intelligence Model |
| title_full_unstemmed | Evaluating the Risk of Metabolic Syndrome Based on an Artificial Intelligence Model |
| title_short | Evaluating the Risk of Metabolic Syndrome Based on an Artificial Intelligence Model |
| title_sort | evaluating the risk of metabolic syndrome based on an artificial intelligence model |
| url | http://dx.doi.org/10.1155/2014/207268 |
| work_keys_str_mv | AT huichen evaluatingtheriskofmetabolicsyndromebasedonanartificialintelligencemodel AT shenghuaxiong evaluatingtheriskofmetabolicsyndromebasedonanartificialintelligencemodel AT xuanren evaluatingtheriskofmetabolicsyndromebasedonanartificialintelligencemodel |