Volume 10, Issue 3 (10-2014)
J Health Syst Res 2014, 10(3): 571-586 |
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Morteza Farahi, Hamid Reza Marateb, Roya Kelishadi, Mohammad Esmaeil Motlagh. A computer-aided Diagnosis System for the Prediction of Overweighs Using Life Style Factors, Socio-economic Status and Family History of Disorders in Children. J Health Syst Res 2014; 10 (3) :571-586
URL: http://hsr.mui.ac.ir/article-1-714-en.html
URL: http://hsr.mui.ac.ir/article-1-714-en.html
1- Student, Biomedical Engineering Department, Faculty of Engineering, University of Isfahan, Iran
2- Assistant Professor, Biomedical Engineering Department, Faculty of Engineering, University of Isfahan, Iran
3- Professor of Pediatrics, Pediatrics Department, Faculty of Medicine and Child Growth and Development Research Center, Isfahan University of Medical Sciences, Isfahan, Iran (Corresponding Author) Email: roya_kelishadi@gmail.com
4- Department of School Health, Bureau of Population, Family and School Health, Ministry of Health and Medical Education, Tehran, Iran
2- Assistant Professor, Biomedical Engineering Department, Faculty of Engineering, University of Isfahan, Iran
3- Professor of Pediatrics, Pediatrics Department, Faculty of Medicine and Child Growth and Development Research Center, Isfahan University of Medical Sciences, Isfahan, Iran (Corresponding Author) Email: roya_kelishadi@gmail.com
4- Department of School Health, Bureau of Population, Family and School Health, Ministry of Health and Medical Education, Tehran, Iran
Abstract: (1317 Views)
Background: Obesity represents one of the most important nutritional problems worldwide.Obesity in childhood can cause variety of health issues such as orthopedic, neurological, pulmonary and gastroenterological disorders in the future, although no side effects were reported from malignant obesity during childhood. In this paper, we presented a computer-aided diagnosis system to predict the obesity based on input features obtained from the life style and other factors of the subjects.Methods: The total number of 9795 subjects (49.17% boy) aged 6 to 18 years taken from the CASPIAN IV study participated in this study. The input parameters of the proposed system were taken from the dietary habit, physical activity, family history, social economic status, and other features. Then, the obesity was predicted using the data mining and artificial intelligence techniques. Feature Selection (FS) methods were also used to improve the performance of the proposed system. The performance of the diagnosis system was assessed based on the hold-out validation framework.Findings: The performance of the classifications method has been validated by hold-out cross validation. Among the different classification techniques tested, SVM with FS showed the best performance. The accuracy and precision of this method were 63.3% and 83.7%, respectively. Some features such as age, physical activities, birth feeding and family history of diabetes mellitus detected as the most effectivefactors with obesity in both gender.Conclusion: Designing of an intelligentdiagnosis system with the input parameter such as life-style, socioeconomic status and genetic information can help predict obesity in children to modify their life-style to improve theirqualityof life in the future. A web-based version of this intelligent system can easily provide the obesity prediction facilities for the families at home.Key Words: Classification; Obesity; Data Mining; Medical Diagnosis System; Artificial Intelligence
Type of Study: Research |
Subject:
education health and promotion
Received: 2020/07/16 | Accepted: 2014/10/15 | Published: 2014/10/15
Received: 2020/07/16 | Accepted: 2014/10/15 | Published: 2014/10/15
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