---
title: "Machine learning classification of pre-metabolic syndrome vs metabolic syndrome in Thai adults"
id: "plos-one-17-classification-of-pre-metabolic-syndrome-and-metabolic-syndrome-using-machine"
canonical_url: "https://medichelpline.com/clinical-feed/plos-one-17-classification-of-pre-metabolic-syndrome-and-metabolic-syndrome-using-machine"
content_type: "clinical_feed_article"
specialty: "Endocrinology"
source_name: "PLOS ONE (Medicine)"
source_url: "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0359283"
published_at: "2026-09-28T14:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Machine learning classification of pre-metabolic syndrome vs metabolic syndrome in Thai adults
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/plos-one-17-classification-of-pre-metabolic-syndrome-and-metabolic-syndrome-using-machine
- **Specialty:** [Endocrinology](https://medichelpline.com/clinical-feed/endocrinology.md)
- **Primary Source:** PLOS ONE (Medicine)
- **Source URL:** [Original Journal Publication](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0359283)
- **Published At:** 2026-09-28T14:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- This single-center retrospective cross-sectional study evaluated whether supervised machine learning (ML) algorithms can distinguish **pre-MetS** from **MetS** using routine clinical and biochemical variables in Thai adults. - The dataset included 657 adults: 359 with pre-metabolic syndrome (pre-MetS) and 298 with metabolic syndrome (MetS) defined by International Diabetes Federation (IDF) criteria; pre-MetS was defined as ≥1 IDF metabolic abnormality without fulfilling full MetS criteria. - Twenty-four features were selected by the Boruta algorithm and included routine measures and derived indices; preprocessing (z-score normalization and feature selection) was applied only within the training set to avoid data leakage. - Ten supervised ML algorithms were evaluated. Models were trained on 80% of the data with repeated 10-fold cross-validation (10 repetitions) for hyperparameter tuning and tested on an independent 20% hold-out set (n = 132). - Ensemble methods outperformed others. **XGBoost** had the highest discrimination (AUC 0.986, 95% CI 0.970–0.997), highest accuracy (0.932, 95% CI 0.885–0.969), and lowest Brier score (0.050, 95% CI 0.026–0.080). - Neural network and random forest showed high performance (AUC 0.948 and 0.942 respectively); support vector machine models showed the lowest discrimination among evaluated algorithms. - SHAP analysis for the XGBoost model identified **systolic blood pressure**, **waist circumference**, **fasting plasma glucose**, sex, and **TyG-WHtR** as the strongest contributors to classification. - Sensitivity analyses removing predictors overlapping MetS diagnostic components reduced discrimination progressively but retained substantial discrimination after excluding overlapping variables. - The authors caution interpretation because influential predictors overlapped with or were derived from MetS diagnostic criteria and the study lacked external validation; they describe findings as exploratory and call for multicenter validation using predictor sets minimizing overlap with diagnostic definitions.
## Clinical Analysis & Structured Key Points
Classification of pre-metabolic syndrome and metabolic syndrome using machine learning and routine clinical variables in Thai adults | PLOS One Browse Subject Areas ? Click through the PLOS taxonomy to find articles in your field. For more information about PLOS Subject Areas, click here . Article Authors Metrics Comments Media Coverage Reader Comments Figures Figures Abstract Background Metabolic syndrome (MetS) and pre-metabolic syndrome (pre-MetS) are increasingly recognized as important stages of metabolic dysfunction in Asian populations. Conventional diagnostic approaches rely on fixed threshold criteria, whereas metabolic dysfunction occurs along a continuous spectrum. Machine learning (ML) can integrate multidimensional clinical data and model nonlinear relationships among clinical variables, potentially providing complementary information for distinguishing metabolic states. This study evaluated ten ML algorithms for distinguishing pre-MetS from MetS in Thai adults and identified features contributing most strongly to model-based classification. Methods In this single-center retrospective cross-sectional study, 657 Thai adults were classified as pre-MetS ( n = 359) or MetS ( n = 298). Pre-MetS was operationally defined as the presence of at least one IDF-defined metabolic abnormality without fulfilling the complete International Diabetes Federation (IDF) criteria for MetS. Twenty-four clinical and biochemical features selected using the Boruta algorithm were evaluated across ten supervised ML algorithms. Models were trained using an 80% training dataset with repeated 10-fold cross-validation (10 repetitions) for hyperparameter tuning and subsequently evaluated using an independent 20% hold-out test dataset ( n = 132). All preprocessing steps, including z-score normalization and feature selection, were performed exclusively within the training dataset to prevent data leakage. Results Among the ten evaluated algorithms, ensemble methods achieved the highest classification performance. XGBoost demonstrated the highest discrimination (AUC = 0.986, 95% CI: 0.970–0.997), highest accuracy (0.932, 95% CI: 0.885–0.969), and lowest Brier score (0.050, 95% CI: 0.026–0.080), followed by the neural network (AUC = 0.948, 95% CI: 0.906–0.983) and random forest (AUC = 0.942, 95% CI: 0.905–0.974). SHAP analysis identified systolic blood pressure, waist circumference, fasting plasma glucose, sex, and TyG-WHtR as the features contributing most strongly to XGBoost classification. Sensitivity analyses demonstrated progressively reduced discrimination after excluding predictors that overlapped with MetS diagnostic components and related derived indices, although substantial discrimination was retained after removal of these overlapping features. Conclusions Among the ten evaluated algorithms, classification performance varied substantially across models, with XGBoost achieving the highest performance and support vector machine models showing the lowest discrimination. These findings indicate that algorithm choice may influence classification performance in metabolic classification tasks. However, the observed performance should be interpreted cautiously because several influential predictors overlap with, or are derived from, the MetS diagnostic criteria, and the study lacked external validation. The findings are therefore exploratory and require validation in multicenter cohorts using predictor sets that minimize overlap with the diagnostic definition before clinical implementation can be considered. Citation: Yaikwawong M, Kamdee K, Chuengsamarn S (2026) Classification of pre-metabolic syndrome and metabolic syndrome using machine learning and routine clinical variables in Thai adults. PLoS One 21(9): e0359283. https://doi.org/10.1371/journal.pone.0359283 Editor: Marwan Salih Al-Nimer, University of Diyala College of Medicine, IRAQ Received: May 26, 2026; Accepted: September 12, 2026; Published: September 28, 2026 Copyright: © 2026 Yaikwawong et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: The de-identified dataset for this study is only available through a controlled-access mechanism because of institutional ethical and privacy restrictions. Data are available upon request from the Institutional Review Board, Srinakharinwirot University, Thailand ( https://ersd.swu.ac.th ), via email ( swuec@g.swu.ac.th ) or telephone (+66 (0)2 649-5000 (ext. 17503, 17504, 17506, 17507)), for researchers who meet the criteria for access to confidential data. To facilitate reproducibility, all analysis scripts—including data preprocessing, feature selection, model development, hyperparameter tuning, performance evaluation, and SHAP analysis—together with package versions, random seeds, and model specifications, are publicly available from the Figshare repository ( https://figshare.com/s/950352bef18da2aa1dde ). Funding: The author(s) received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist. Abbreviations: AUC, area under the receiver operating characteristic curve; BMI, body mass index; CI, confidence interval; FPG, fasting plasma glucose; HbA1c, glycated hemoglobin; HDL-C, high-density lipoprotein cholesterol; HOMA-β, homeostatic model assessment of beta-cell function; HOMA-IR, homeostatic model assessment for insulin resistance; IDF, International Diabetes Federation; LDL-C, low-density lipoprotein cholesterol; MetS, metabolic syndrome; METS-IR, metabolic score for insulin resistance; ML, machine learning; NPV, negative predictive value; PPV, positive predictive value; pre-MetS, pre-metabolic syndrome; SBP, systolic blood pressure; T2DM, type 2 diabetes mellitus; TG, triglycerides; TyG, triglyceride-glucose index; TyG-BMI, triglyceride-glucose body mass index; TyG-WC, triglyceride-glucose waist circumference; TyG-WHtR, triglyceride-glucose waist-to-height ratio; WC, waist circumference; WHtR, waist-to-height ratio; XGBoost, extreme gradient boosting Introduction Cardiometabolic diseases, including type 2 diabetes mellitus (T2DM) and metabolic syndrome (MetS), represent a major global health burden and contribute substantially to morbidity, mortality, and healthcare costs worldwide. MetS is characterized by a cluster of interrelated metabolic abnormalities, including central obesity, dyslipidemia, hypertension, and hyperglycemia, which increase the risk of cardiovascular disease and diabetic complications [ 1 , 2 ]. Globally, the prevalence of MetS has increased substantially during recent decades in parallel with rising rates of obesity, population aging, physical inactivity, and unhealthy dietary patterns [ 3 , 4 ]. The consequences extend beyond clinical complications, as MetS is associated with increased healthcare utilization, long-term pharmacological treatment, reduced productivity, diminished quality of life, and substantial economic burden on healthcare systems [ 5 ]. Consequently, early recognition of individuals with emerging metabolic dysfunction has become a public health priority aimed at reducing future cardiometabolic morbidity and mortality [ 3 , 6 ]. The burden of MetS is particularly pronounced in Asian populations, where rapid economic development, urbanization, and lifestyle transitions have accelerated the prevalence of obesity and diabetes-related disorders. In Southeast Asia, increasing consumption of energy-dense diets, reduced physical activity, and demographic aging have contributed to a growing cardiometabolic disease burden [ 7 , 8 ]. Thailand has experienced similar trends, with national health surveys demonstrating a substantial burden of metabolic abnormalities among adults. Data from the fourth Thai National Health Examination Survey (NHES IV) showed that MetS affected 23.2% of Thai adults aged ≥20 years, with prevalence estimates of 19.5% in men and 26.8% in women [ 9 ]. Similarly, a nationally representative study of Thai adults aged ≥35 years reported an age-standardized MetS prevalence of 24.0% according to IDF criteria, although prevalence increased to 32.6% when the NCEP-ATP III criteria were applied [ 10 ]. These differences highlight the influence of diagnostic definitions on estimates of the population burden of MetS. The associated healthcare expenditures, loss of productivity, and increased risk of cardiovascular disease and T2DM place considerable strain on individuals, families, and healthcare systems [ 11 ]. Therefore, improved strategies for identifying individuals at different stages of metabolic dysfunction are needed in the Thai context. The metabolic abnormalities underlying MetS often accumulate over time rather than emerging simultaneously. Before fulfilling the complete diagnostic criteria for MetS, individuals may exhibit an intermediate metabolic state characterized by the presence of some, but not all, metabolic abnormalities. This transitional state may represent an early phase of metabolic dysfunction and a potential opportunity for preventive intervention [ 7 ]. This intermediate condition has been described as pre-metabolic syndrome (pre-MetS). Although pre-MetS has gained increasing attention as a potentially important stage in the trajectory toward overt metabolic disease, it is not as universally standardized as MetS, and definitions vary among studies [ 8 , 9 ]. Consequently, a clear operational definition is required for research purposes to facilitate consistent classification and interpretation of findings. In the present study, pre-MetS was operationally defined as the presence of at least one IDF-defined metabolic abnormality without fulfilling the complete IDF criteria for MetS. Central obesity was not required for classification as pre-MetS [ 8 , 10 ]. The clinical significance of pre-MetS lies in its potential to represent an early stage of metabolic deterioration rather than a metabolically healthy state. Individuals with pre-MetS may already demonstrate evidence of insulin resistance, central adiposity, dyslipidemia, or impaired glucose regulation and may be at elevated risk of progression to MetS, T2DM, and cardiovascular disease [ 8 ]. Therefore, pre-MetS was selected as the comparison group because it represents a clinically relevant intermediate stage along the continuum of metabolic dysfunction. Distinguishing individuals with pre-MetS from those with established MetS may provide insight into differences in metabolic status and identify opportunities for preventive intervention before further metabolic deterioration occurs [ 9 ]. Conventional diagnosis of MetS relies on established threshold-based criteria such as those proposed by the International Diabetes Federation (IDF), which classify individuals according to predefined cut-off values for waist circumference, blood pressure, plasma glucose, triglycerides, and HDL-C [ 11 ]. These criteria remain an established clinical framework and provide a simple and practical approach to MetS classification. However, metabolic dysfunction exists on a biological continuum, whereas threshold-based definitions categorize individuals into discrete groups. As a result, individuals with similar underlying metabolic profiles may be classified differently depending on whether specific diagnostic cut-offs are exceeded [ 12 ]. Furthermore, conventional threshold-based criteria do not quantify the relative contribution or potential interactions among metabolic variables and may not fully represent the multidimensional nature of metabolic dysfunction [ 13 , 14 ]. Therefore, although established diagnostic criteria remain the clinical standard for MetS classification, complementary analytical approaches may provide additional insight into how multiple routinely collected variables collectively characterize different stages of metabolic dysfunction. Machine learning (ML) provides a complementary analytical approach that can integrate numerous demographic, anthropometric, and biochemical variables simultaneously while identifying complex nonlinear relationships and interactions among features. Importantly, the purpose of ML in the present study was not to replace established IDF diagnostic criteria. Rather, ML was evaluated as a tool for classification and characterization of existing metabolic states, allowing assessment of how routinely collected clinical variables collectively distinguish individuals with pre-MetS from those with MetS. Consistent with the cross-sectional study design, the developed models were intended to classify existing metabolic status rather than predict future disease progression or cardiometabolic outcomes. In addition, ML models can provide information regarding feature importance, thereby identifying variables that contribute most strongly to model-based metabolic classification. Such information may improve understanding of the metabolic patterns associated with more advanced metabolic states and support the development of simplified screening strategies using routinely available clinical data [ 15 – 17 ] . Among the variables increasingly investigated in cardiometabolic research are composite indices that combine anthropometric and biochemical parameters. The triglyceride-glucose (TyG) index and its derivatives, including TyG-waist circumference (TyG-WC) and TyG-waist-to-height ratio (TyG-WHtR), have emerged as practical surrogate markers of insulin resistance and visceral adiposity and have demonstrated associations with MetS, T2DM, and cardiovascular risk [ 18 – 20 ]. These indices are particularly attractive because they can be calculated using inexpensive and routinely available clinical measurements. However, several composite indices incorporate variables that overlap with diagnostic components of MetS, particularly waist circumference, triglycerides, and fasting plasma glucose [ 11 , 18 – 20 ]. Consequently, apparent improvements in classification performance may partly reflect overlap with the reference definition rather than entirely independent discriminatory information. Classification performance should therefore be interpreted cautiously, and sensitivity analyses are necessary to evaluate the extent to which such overlap influences model discrimination [ 21 ]. Unlike previous studies that have primarily focused on distinguishing MetS from healthy individuals, the present study specifically evaluated classification between pre-MetS and MetS, representing a more clinically challenging distinction along the spectrum of metabolic dysfunction. Furthermore, we systematically compared ten machine-learning algorithms and conducted sensitivity analyses to assess the influence of diagnostic overlap on classification performance. These methodological approaches provide additional insight into the potential utility and limitations of machine learning for metabolic classification in Thai adults. Despite growing interest in ML applications for metabolic disease assessment, relatively limited evidence is available from Southeast Asian populations, particularly Thailand. Population-specific investigation is important because Asian individuals may develop insulin resistance, visceral adiposity, and cardiometabolic complications at lower body mass index levels than Western populations. These differences in body composition and metabolic characteristics suggest that findings derived from Western cohorts may not be directly generalizable to Thai adults [ 22 , 23 ]. Furthermore, evidence regarding ML-based classification specifically between pre-MetS and MetS remains limited. Therefore, this study aimed to: (1) compare the clinical and metabolic characteristics of Thai adults with pre-MetS and MetS; (2) develop and evaluate multiple machine-learning models for classification of these existing metabolic states using routinely collected clinical variables; and (3) identify the features contributing most strongly to model-based classification of metabolic status. Methods Study design and ethical approval This single-center retrospective cross-sectional study analyzed routinely collected clinical data from individuals attending the outpatient clinic at HRH Princess Maha Chakri Sirindhorn Medical Center, Srinakharinwirot University, Nakhon Nayok, Thailand, between January 2021 and December 2022. The study was approved by the Institutional Review Board of Srinakharinwirot University (SWUEC-683072) and was conducted in accordance with the Declaration of Helsinki. Written informed consent permitting the use of clinical data for future research was obtained from all participants at the time of routine clinical enrollment. For the present study, de-identified clinical data were retrospectively extracted from the hospital database on 20 September 2025. Because only anonymized data were analyzed and no participant contact occurred, the Institutional Review Board waived the requirement for additional informed consent for this retrospective analysis. Participants A total of 720 consecutive adults attending the outpatient clinic between January 2021 and December 2022 were screened for eligibility . Consecutive sampling was used, whereby all potentially eligible individuals attending the clinic during the study period were considered for inclusion rather than selecting participants according to predetermined sampling quotas. All participants were recruited from a single tertiary-care academic medical center. Demographic, clinical, anthropometric, and biochemical data were retrospectively obtained from the institutional electronic medical record system. Eligible participants were aged ≥18 years, classified as having pre-metabolic syndrome (pre-MetS) or metabolic syndrome (MetS), and had complete demographic, anthropometric, clinical, and biochemical data required for analysis. Individuals younger than 18 years were excluded because diagnostic criteria for MetS, waist circumference thresholds, and cardiometabolic risk profiles differ substantially between pediatric and adult populations. Restricting the study to adults ensured consistent application of the International Diabetes Federation (IDF) criteria and reduced population heterogeneity. Participants were excluded if they were pregnant or lactating; had an acute illness within the preceding month; had chronic inflammatory disease, active infection, malignancy, severe hepatic or renal dysfunction, or endocrine disorders known to affect metabolic status; or had incomplete clinical or biochemical data. These exclusion criteria were identified from electronic medical records, physician diagnoses, laboratory test results, and clinician documentation available in the hospital information system. Of the 720 individuals screened, 63 were excluded because of incomplete biochemical data ( n = 41), incomplete anthropometric measurements ( n = 22). Consequently, 657 participants were included in the final analysis, comprising 359 individuals with pre-MetS and 298 with MetS. The participant selection process is illustrated in S1 Fig . Because this study analyzed an existing retrospective clinical database, no formal a priori sample size calculation was performed. The available sample size was determined by the number of eligible participants during the study period. Sample size considerations for machine-learning classification There is no universal minimum sample size applicable to all machine-learning classification algorithms because the required sample size depends on factors including the number and complexity of features, model architecture, outcome prevalence, and the expected signal-to-noise ratio. In the present study, 657 participants were available for analysis, including 298 participants with MetS and 359 with pre-MetS . The dataset was subsequently divided using an 80%/20% stratified split, result
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