Genomics Inform.  2018 Dec;16(4):e31. 10.5808/GI.2018.16.4.e31.

Metabolic Syndrome Prediction Using Machine Learning Models with Genetic and Clinical Information from a Nonobese Healthy Population

Affiliations
  • 1Department of Surgery, Seoul National University Hospital, Healthcare System Gangnam Center, Seoul 06236, Korea.
  • 2DNALink, Inc., Seoul 03759, Korea.
  • 3Department of Family Medicine, Seoul National University Hospital, Healthcare System Gangnam Center, Seoul 06236, Korea.
  • 4Department of Internal Medicine, Seoul National University Hospital, Healthcare System Gangnam Center, Seoul 06236, Korea. cshmed@snuh.org

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.

Keyword

genetic polymorphism; machine learning; metabolic syndrome

MeSH Terms

Alcohol Drinking
Bays
Body Mass Index
Classification
Life Style
Machine Learning*
Polymorphism, Genetic
Prevalence
ROC Curve
Sensitivity and Specificity
Smoke
Smoking
Smoke
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