Identifying Metabolic Syndrome Easily and Cost Effectively Using Non-Invasive Methods with Machine Learning Models

Abstract

The objective of this study was to employ machine learning (ML) models utilizing non-invasive factors to achieve early and low-cost identification of MetS in a large physical examination population. Patients and Methods:The study enrolled 9171 participants who underwent physical examinations at Northern Jiangsu People's Hospital in 2009 and 2019, to determine MetS based on criteria established by the Chinese Diabetes Society. Non-invasive characteristics such as gender, age, body mass index (BMI), systolic blood pressure (SBP), and diastolic blood pressure (DBP) were collected and used as input variables to train and evaluate ML models for MetS identification. Several ML models were used for MetS identification, including logistic regression (LR), k-nearest neighbors algorithm (k-NN), naive bayesian (NB), decision tree (DT), random forest (RF), artificial neural network (ANN), and support vector machine (SVM). Results: Our ML models all showed good performance in the 10-fold cross-validation except for the SVM model. In the external validation, the NB model exhibited the best performance with an AUC of 0.976, accuracy of 0.923, sensitivity of 98.32%, and specificity of 91.32%. Conclusion:This study proposed a new non-invasive method for early and low-cost identification of MetS by using ML models. This approach has the potential to serve as a highly sensitive, convenient, and cost-effective tool for large-scale MetS screening.

Enhancing Metabolic Syndrome Detection through Blood Tests Using Advanced Machine Learning

“…An outer k-fold cross-validation loop is used in nested cross-validation to offer a comprehensive assessment of the best model's performance. Each outer fold uses an inner crossvalidation loop to fine-tune the model's parameters at the same time [23].…”

“…This predisposition to cardiovascular diseases and type 2 diabetes has further broadened to include complications such as non-alcoholic fatty liver disease, chronic prothrombotic and proinflammatory states, and sleep apnoea. Despite efforts by various global health organizations, achieving a universal consensus on the precise definition of MetS remains a significant challenge for healthcare practitioners and researchers [5,23,24]. The widespread prevalence of MetS leads to substantial socio-economic costs due to its associated significant morbidity and mortality.…”