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Metabolic syndrome prediction using non-invasive and dietary parameters based on a support vector machine
Sahar Mohseni-Takalloo1, Hassan Mozaffari-Khosravi2, Hadis Mohseni3
1Research Center for Food Hygiene and Safety, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran; Department of Nutrition, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran; School of Public Health, Bam University of Medical Sciences, Bam, Iran.
Machine learning models using non-invasive, low-cost (NI&LC) and dietary data can effectively predict metabolic syndrome (MetS). Support vector machine (SVM) algorithms show promise for early MetS detection in prevention programs.
Area of Science:
- Biomedical Informatics
- Public Health
- Machine Learning
Background:
- Metabolic syndrome (MetS) is a key indicator for chronic disease risk, including cardiovascular disease and diabetes.
- Early detection of MetS is crucial for effective prevention strategies.
- Machine learning approaches show potential for MetS diagnosis.
Purpose of the Study:
- To develop and evaluate Support Vector Machine (SVM) based prediction models for Metabolic Syndrome (MetS).
- To utilize non-invasive and low-cost (NI&LC) and dietary parameters for MetS prediction.
- To assess the efficacy of machine learning models in identifying individuals at risk for MetS.
Main Methods:
- A population-based dataset of 4596 participants from the Shahedieh cohort study was analyzed.
- Extremely Randomized Trees Classifier was employed for feature selection from NI&LC and dietary data.
- SVM algorithms were used to build prediction models, with performance evaluated using accuracy, sensitivity, specificity, and ROC curves.
Main Results:
- MetS prevalence was 14% in men and 22% in women.
- Key NI&LC predictors included waist circumference, BMI, waist-to-height ratio, waist-to-hip ratio, systolic, and diastolic blood pressure.
- SVM models achieved accuracies of 78.4% (men) and 63.5% (women) using NI&LC data, with sensitivity of 81.2% and 75.3% respectively.
- Incorporating dietary features improved model accuracy in women by 3.7%.
Conclusions:
- SVM algorithms demonstrate significant potential for the early detection of Metabolic Syndrome (MetS) using NI&LC parameters.
- These predictive models are suitable for integration into prevention programs, clinical settings, and personal health applications.
- Machine learning-based approaches offer a valuable tool for proactive management of MetS risk.
