title: Exploring the effects of intervention for those at high risk of developing type 2 diabetes using a computer simulation.
abstract: A simulation based computational method was conducted to reflect the effect of intervention for those at high risk of type 2 diabetes. Hierarchy Support Vector Machines (H-SVMs) were used to classify high risk. The proportion transitioning from the high risk state to moderate state, low state or the normal state was calculated. When Body Mass Index (BMI) decreased by 5% (weight loss 3-5kg), the proportion of Class A transferring to a lower state was 15-25%, and risk also appeared reduced for Class B1. In Class C, when cholesterol (CHOL) was decreased by 2.5% (0.13-0.34mmol/L), 10-25% transitioned to a lower risk state. The method could help determine risk transition by the adjustment of sensitive risk factors. This might provide the basis for implementing intervention in cases in a high risk state.
authors.affiliation: School of Information and Electronics, Beijing Institute of Technology, 100081 Beijing, China.
authors.forename: Senlin
authors.initials: S
authors.lastname: Luo
authors.affiliation: School of Information and Electronics, Beijing Institute of Technology, 100081 Beijing, China. Electronic address: 3120100321@bit.edu.cn.
authors.forename: Songjing
authors.initials: S
authors.lastname: Chen
authors.affiliation: School of Information and Electronics, Beijing Institute of Technology, 100081 Beijing, China.
authors.forename: Limin
authors.initials: L
authors.lastname: Pan
authors.affiliation: Department of Cell Biology, The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Ministry of Health, 100730 Beijing, China.
authors.forename: Tiemei
authors.initials: T
authors.lastname: Zhang
authors.affiliation: School of Information and Electronics, Beijing Institute of Technology, 100081 Beijing, China.
authors.forename: Longfei
authors.initials: L
authors.lastname: Han
authors.affiliation: School of Information and Electronics, Beijing Institute of Technology, 100081 Beijing, China.
authors.forename: Yue
authors.initials: Y
authors.lastname: Wang
authors.affiliation: School of Information and Electronics, Beijing Institute of Technology, 100081 Beijing, China.
authors.forename: Qamas Gul Khan
authors.initials: QG
authors.lastname: Safi
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002784:Cholesterol / Q000097:blood; D003198:Computer Simulation; D003924:Diabetes Mellitus, Type 2 / Q000453:epidemiology / Q000503:physiopathology / Q000628:therapy; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015203:Reproducibility of Results; D012307:Risk Factors; D060388:Support Vector Machine; D015431:Weight Loss / Q000502:physiology; D055815:Young Adult
chemical_list: D002784:Cholesterol
keywords: High risk; Intervention; Simulated computation; Transition; Type 2 diabetes