Which diagnostic criteria of metabolic syndrome are predictors of cardiovascular diseases in elderly populations?

Ladla, Arinrada; Tongkrajai, Pramote; Srisaenpang, Sompong; Siviroj, Penprapa; Yutthakasemsunt, Surakrant; Tiamkao, Somsak; Chotmongkol, Verajit; Sawanyawisuth, Kittisak

doi:10.1016/j.jcte.2020.100248

Cited by 1 publication

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“…It also includes measures for carbon dioxide, potassium, chloride, triglyceride levels, and cholesterol. Comprehensive metabolic blood tests have identified these as the most common metabolic syndrome criteria [ 47 ]. Furthermore, triglyceride and cholesterol levels were defined as features in the utilized dataset.…”

Section: Metabolic Data Classification Experimental Results and Model...mentioning

confidence: 99%

Machine Learning Approach for Metabolic Syndrome Diagnosis Using Explainable Data-Augmentation-Based Classification

et al. 2022

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Metabolic syndrome (MetS) is a cluster of risk factors including hypertension, hyperglycemia, dyslipidemia, and abdominal obesity. Metabolism-related risk factors include diabetes and heart disease. MetS is also linked to numerous cancers and chronic kidney disease. All of these variables raise medical costs. Developing a prediction model that can quickly identify persons at high risk of MetS and offer them a treatment plan is crucial. Early prediction of metabolic syndrome will highly impact the quality of life of patients as it gives them a chance for making a change to the bad habit and preventing a serious illness in the future. In this paper, we aimed to assess the performance of various algorithms of machine learning in order to decrease the cost of predictive diagnoses of metabolic syndrome. We employed ten machine learning algorithms along with different metaheuristics for feature selection. Moreover, we examined the effects of data augmentation in the prediction accuracy. The statistics show that the augmentation of data after applying feature selection on the data highly improves the performance of the classifiers.

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Section: Metabolic Data Classification Experimental Results and Model...mentioning

confidence: 99%

Machine Learning Approach for Metabolic Syndrome Diagnosis Using Explainable Data-Augmentation-Based Classification

et al. 2022

View full text Add to dashboard Cite

show abstract

Which diagnostic criteria of metabolic syndrome are predictors of cardiovascular diseases in elderly populations?

Cited by 1 publication

References 27 publications

Machine Learning Approach for Metabolic Syndrome Diagnosis Using Explainable Data-Augmentation-Based Classification

Machine Learning Approach for Metabolic Syndrome Diagnosis Using Explainable Data-Augmentation-Based Classification

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