Fatemeh Taheri - Cardiovascular Diseases Research Centre, Department of Pediatric, Birjand University of Medical Sciences, Birjand, Iran.
Vahide Babaiyan - Department of Computer Engineering, Birjand University of Technology, Birjand, Iran
Mohsen Saffarian - Department of Industrial Engineering, Birjand University of Technology, Birjand, Iran
Seyed Mahmood Kazemi - Department of Industrial Engineering, Birjand University of Technology, Birjand, Iran
Kokab Namakin - Cardiovascular Diseases Research Centre, Department of Pediatric, Birjand University of Medical Sciences, Birjand, Iran.
Toba Kazemi - Cardiovascular Diseases Research Centre, Department of Cardiology, Birjand University of Medical Sciences, Birjand, Iran.
Azra Ramezankhani - Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Metabolic Syndrome (MS) is the collection of risk factors for coronary artery disease (CAD). It is responsible for cardiovascular disease (CVD), type ۲ diabetes, cancer, renal and mental diseases with the transition from childhood to adulthood. The MS is increasing in recent decades in different societies, especially in Iran. This study was conducted to determine the predictive factors of MS in children and adolescents in Birjand (Capital of South Khorasan Province, Iran) using a data mining approach. The four different analyses for females and males (۶-۱۱ and ۱۲-۱۸ year-old groups) were carried out using three popular decision tree models. The most important prognostic factors for MS were high level of TG and low level of DBP and HDL in ۶-۱۱-year-old group, and high level of waist circumference (WC) and low level of TG for ۱۲-۱۸-year-old group. The most important factors were TG and HDL in females and WC and TG in males. Raising teens and families' awareness of the risk factors, screening children and teens, monitoring and controlling the risk factors through life style correction such as more physical activity and healthy eating are recommended.

Index terms, Metabolic Syndrome, Data mining, Decision Tree, School-Age Population