Applications of Machine Learning Algorithm to Predict Metabolic Syndrome in Taiwanese Children and Adolescents

• Main Authors: Chieh-Wen Chen, 陳潔雯
• Other Authors: Emily Chia-Yu Su
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/4ex82a

碩士 === 臺北醫學大學 === 醫學資訊研究所 === 102 === Metabolic syndrome consists of a cluster of the dangerous risk factors of cardiovascular diseases and diabetes. Due to the increasing prevalence of obesity in children related to unhealthy diet and lifestyle, the International Diabetes Federation (IDF) published diagnosis criteria of metabolic syndrome in children and adolescents in 2007. Yet, the IDF also recognized that such diagnostic criteria may not be applicable among the various racial, gender and age differences in this unique population subjected to development of adult physical and sexual characteristics. The aim of this study is using machine learning Algorithms to predict metabolic syndrome in Taiwanese children and adolescents for early screening and diagnosis. Total 2,362 medical health records of children and adolescents of 10 to 16 years of age from one health examination center are collected for this study, and there was 162 records enrolled for this analysis (81 records matched the diagnostic criteria of metabolic syndrome, another 107 records which did not matched the diagnostic criteria was extracted by using random sample selection. Five-fold cross-validation is used to evaluate our experiment results. The presence of metabolic syndrome is diagnosed based on criteria defined by the IDF and presence of obesity identified by body mass index (BMI) according to Taiwanese children and adolescent obesity definition published by the Ministry of Health and Welfare (Taiwan 2002). The study extracted eighteen features obtained from physical measurements and biochemical blood tests for prediction. The features include the following: BMI, blood pressure, fasting serum glucose (FG), lipid profile, thyroid function, and hemogram. For model construction, we apply WEKA 3.6, in which classifiers including decision trees (DT), random forests (RF), support vector machines (SVM), multilayer perceptron (MLP) and logistic regression (LR), are adopted to predict metabolic syndrome. We evaluated accuracy, sensitivity, specificity, and area under receiver operator characteristic curve (AUC) to assess predictive performance. Five-fold cross-validation is used to evaluate our experiment results. We conclude that applying support vector machine (LibSVM) to predict metabolic syndrome can serve as an effective method to assist in establishing a clinical decision making system with AUC with 0.967. Both SVM and decision trees can reached the highest accuracy rate with 90.9%. In addition, BMI, TG, FG, LDL and diastolic blood pressure are selected as the most effective features in the diagnosis of metabolic syndrome in children and adolescents between the ages of 10 and 16 years old in Taiwan.