Metabolic Syndrome Prediction Using Machine Learning Models with Genetic and Clinical Information from a Nonobese Healthy Population
- Author:
Eun Kyung CHOE
1
;
Hwanseok RHEE
;
Seungjae LEE
;
Eunsoon SHIN
;
Seung Won OH
;
Jong Eun LEE
;
Seung Ho CHOI
Author Information
- Publication Type:Original Article
- Keywords: genetic polymorphism; machine learning; metabolic syndrome
- MeSH: Alcohol Drinking; Bays; Body Mass Index; Classification; Life Style; Machine Learning; Polymorphism, Genetic; Prevalence; ROC Curve; Sensitivity and Specificity; Smoke; Smoking
- From:Genomics & Informatics 2018;16(4):e31-
- CountryRepublic of Korea
- Language:English
- Abstract: The prevalence of metabolic syndrome (MS) in the nonobese population is not low. However, the identification and risk mitigation of MS are not easy in this population. We aimed to develop an MS prediction model using genetic and clinical factors of nonobese Koreans through machine learning methods. A prediction model for MS was designed for a nonobese population using clinical and genetic polymorphism information with five machine learning algorithms, including naïve Bayes classification (NB). The analysis was performed in two stages (training and test sets). Model A was designed with only clinical information (age, sex, body mass index, smoking status, alcohol consumption status, and exercise status), and for model B, genetic information (for 10 polymorphisms) was added to model A. Of the 7,502 nonobese participants, 647 (8.6%) had MS. In the test set analysis, for the maximum sensitivity criterion, NB showed the highest sensitivity: 0.38 for model A and 0.42 for model B. The specificity of NB was 0.79 for model A and 0.80 for model B. In a comparison of the performances of models A and B by NB, model B (area under the receiver operating characteristic curve [AUC] = 0.69, clinical and genetic information input) showed better performance than model A (AUC = 0.65, clinical information only input). We designed a prediction model for MS in a nonobese population using clinical and genetic information. With this model, we might convince nonobese MS individuals to undergo health checks and adopt behaviors associated with a preventive lifestyle.
