Repository of Research and Investigative Information

Repository of Research and Investigative Information

Shahid Sadoughi University of Medical Sciences

Metabolic syndrome prediction using non-invasive and dietary parameters based on a support vector machine

(2024) Metabolic syndrome prediction using non-invasive and dietary parameters based on a support vector machine. Nutrition Metabolism and Cardiovascular Diseases. pp. 126-135. ISSN 0939-4753

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Abstract

Background and aims: Metabolic syndrome (MetS) is a widely used index for finding people at risk for chronic diseases, including cardiovascular disease and diabetes. Early detection of MetS is especially important in prevention programs. Relying on previous studies that suggest machine learning methods as a valuable approach for diagnosing MetS, this study aimed to develop MetS prediction models based on support vector machine (SVM) algorithms, applying non-invasive and low-cost (NI&LC), and also dietary parameters.Methods and results: This population-based research was conducted on a large dataset of 4596 participants within the framework of the Shahedieh cohort study. An Extremely Randomized Trees Classifier was used to select the most effective features among NI&LC and dietary data. The prediction models were developed based on SVM algorithms, and their performance was assessed by accuracy, sensitivity, specificity, positive prediction value, negative prediction value, f1 score, and receiver operating characteristic curve. MetS was diagnosed in 14 of men and 22 of women. Among NI&LC features, waist circumference, body mass index, waist-to-height ratio, waist-to-hip ratio, systolic blood pressure, and diastolic blood pressure were the most predictive variables. By using NI&LC features, models with 78.4 and 63.5 accuracy and 81.2 and 75.3 sensitivity were yielded for men and women, respectively. By incorporating NI&LC and dietary features, the accuracy of the model in women improved by 3.7.Conclusions: SVM algorithms had promising potential for early detection of MetS relying on NI&LC parameters. These models can be used in prevention programs, clinical practice, and personal applications.(c) 2023 The Italian Diabetes Society, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition and the Department of Clinical Medicine and Surgery, Federico II University. Published by Elsevier B.V. All rights reserved.

Item Type: Article
Keywords: Metabolic syndrome Machine learning Support vector machine SVM persian cohort risk-factors iran association consumption prevalence patterns adults Cardiovascular System & Cardiology Endocrinology & Metabolism Nutrition & Dietetics
Page Range: pp. 126-135
Journal or Publication Title: Nutrition Metabolism and Cardiovascular Diseases
Journal Index: WoS
Volume: 34
Number: 1
Identification Number: https://doi.org/10.1016/j.numecd.2023.08.018
ISSN: 0939-4753
Depositing User: Mr mahdi sharifi
URI: http://eprints.ssu.ac.ir/id/eprint/29420

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