Prediction of metabolic syndrome: A machine learning approach to help primary prevention

Diabetes Res Clin Pract. 2022 Sep:191:110047. doi: 10.1016/j.diabres.2022.110047. Epub 2022 Aug 24.

Abstract

Aims: To describe the performance of machine learning (ML) applied to predict future metabolic syndrome (MS), and to estimate lifestyle changes effects in MS predictions.

Methods: We analyzed data from 17,182 adults attending a checkup program sequentially (37,999 visit pairs) over 17 years. Variables on sociodemographic attributes, clinical, laboratory, and lifestyle characteristics were used to develop ML models to predict MS [logistic regression, linear discriminant analysis, k-nearest neighbors, decision trees, Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting]. We have tested the effects of changes in lifestyle in MS prediction at individual levels.

Results: All models showed adequate calibration and good discrimination, but the LGBM showed better performance (Sensitivity = 87.8 %, Specificity = 70.2 %, AUC-ROC = 0.86). Causal inference analysis showed that increasing physical activity level and reducing BMI by at least 2 % had an effect of reducing the predicted probability of MS by 3.8 % (95 % CI = -4.8 %; -2.7 %).

Conclusion: ML models based on data from a checkup program showed good performance to predict MS and allowed testing for effects of lifestyle changes in this prediction. External validation is recommended to verify models' ability to identify at-risk individuals, and potentially increase their engagement in preventive measures.

Keywords: Artificial intelligence; Machine learning; Metabolic syndrome; Primary prevention; Risk prediction.

MeSH terms

  • Adult
  • Humans
  • Logistic Models
  • Machine Learning
  • Metabolic Syndrome* / diagnosis
  • Metabolic Syndrome* / epidemiology
  • Metabolic Syndrome* / prevention & control
  • Primary Prevention