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

Abstract

Wei Xu,1,* Zikai Zhang,2,* Kerong Hu,1,* Ping Fang,1 Ran Li,1 Dehong Kong,1 Miao Xuan,1 Yang Yue,3 Dunmin She,4,5 Ying Xue1 1Department of Endocrinology and Metabolism, Tongji Hospital, School of Medicine, Tongji University, Shanghai, People’s Republic of China; 2Department of Oncology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, People’s Republic of China; 3School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia; 4Clinical Medical College, Yangzhou University, Yangzhou, Jiangsu, People’s Republic of China; 5Department of Endocrinology, Northern Jiangsu People’s Hospital Affiliated to Yangzhou University, Yangzhou, Jiangsu, People’s Republic of China*These authors contributed equally to this workCorrespondence: Ying Xue, Department of Endocrinology and Metabolism, Tongji Hospital, School of Medicine, Tongji University, No. 389, Xincun Road, Shanghai, People’s Republic of China, Tel [phone], Email [email protected] Dunmin She, Clinical Medical College, Yangzhou University, No. 98, Nantong West Road, Yangzhou, Jiangsu, People’s Republic of China, Email [email protected]: 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.Keywords: metabolic syndrome, machine learning methods, non-invasive method, naive Bayesian

Keywords

• metabolic syndrome
• machine learning methods
• non-invasive method
• naive bayesian