Early prediction of body composition parameters on metabolically unhealthy in the Chinese population via advanced machine learning

Early prediction of body composition parameters on metabolically unhealthy in the Chinese population via advanced machine learning

BackgroundMetabolic syndrome (Mets) is considered a global epidemic of the 21st century, predisposing to cardiometabolic diseases. This study aims to describe and compare the body composition profiles between metabolic healthy (MH) and metabolic unhealthy (MU) phenotype in normal and obesity populat...

Full description

Bibliographic Details
Main Authors:	Xiujuan Deng, Lin Qiu, Xin Sun, Hui Li, Zejiao Chen, Min Huang, Fangxing Hu, Zhenyi Zhang
Format:	Article
Language:	English
Published:	Frontiers Media S.A. 2023-08-01
Series:	Frontiers in Endocrinology
Subjects:	metabolic syndrome metabolic unhealthy body composition machine learning SHapley additive exPlanations
Online Access:	https://www.frontiersin.org/articles/10.3389/fendo.2023.1228300/full

Similar Items

Long-term effects of particulate matter on incident cardiovascular diseases in middle-aged and elder adults: The CHARLS cohort study
by: Shiyun Lv, et al.
Published: (2023-09-01)

An Explainable Machine Learning Framework for Intrusion Detection Systems
by: Maonan Wang, et al.
Published: (2020-01-01)

Explainable machine learning model for predicting skeletal muscle loss during surgery and adjuvant chemotherapy in ovarian cancer
by: Wen‐Han Hsu, et al.
Published: (2023-10-01)

Machine learning-based detection of cervical spondylotic myelopathy using multiple gait parameters
by: Xinyu Ji, et al.
Published: (2023-06-01)

Explainable Machine Learning Model for Predicting First-Time Acute Exacerbation in Patients with Chronic Obstructive Pulmonary Disease
by: Chew-Teng Kor, et al.
Published: (2022-02-01)

Machine Learning Analysis of Thermal Performance Indicator of Heat Exchangers with Delta Wing Vortex Generators
by: Zafer Yavuz Aksöz, et al.
Published: (2024-03-01)

Development and validation of an interpretable radiomic nomogram for severe radiation proctitis prediction in postoperative cervical cancer patients
by: Chaoyi Wei, et al.
Published: (2023-01-01)

The relationship between heavy metals and metabolic syndrome using machine learning
by: Jun Yao, et al.
Published: (2024-04-01)

Severity Prediction of Highway Crashes in Saudi Arabia Using Machine Learning Techniques
by: Ibrahim Aldhari, et al.
Published: (2022-12-01)

An Explainable Supervised Machine Learning Model for Predicting Respiratory Toxicity of Chemicals Using Optimal Molecular Descriptors
by: Keerthana Jaganathan, et al.
Published: (2022-04-01)

Latent trajectories of frailty and risk prediction models among geriatric community dwellers: an interpretable machine learning perspective
by: Yafei Wu, et al.
Published: (2022-11-01)

Assessment of a New Solar Radiation Nowcasting Method Based on FY-4A Satellite Imagery, the McClear Model and SHapley Additive exPlanations (SHAP)
by: Dongyu Jia, et al.
Published: (2023-04-01)

Unraveling the Dysbiosis of Vaginal Microbiome to Understand Cervical Cancer Disease Etiology—An Explainable AI Approach
by: Karthik Sekaran, et al.
Published: (2023-04-01)

Establishment and Validation of Predictive Model of Tophus in Gout Patients
by: Tianyi Lei, et al.
Published: (2023-02-01)

Machine Learning to Predict the Response to Lenvatinib Combined with Transarterial Chemoembolization for Unresectable Hepatocellular Carcinoma
by: Jun Ma, et al.
Published: (2023-01-01)

Potential Determinants for Radiation-Induced Lymphopenia in Patients With Breast Cancer Using Interpretable Machine Learning Approach
by: Hao Yu, et al.
Published: (2022-06-01)

Model of Storm Surge Maximum Water Level Increase in a Coastal Area Using Ensemble Machine Learning and Explicable Algorithm
by: Kun Sun, et al.
Published: (2023-12-01)

Explainable Anomaly Detection Framework for Maritime Main Engine Sensor Data
by: Donghyun Kim, et al.
Published: (2021-07-01)

A hybrid strategy of AutoML and SHAP for automated and explainable concrete strength prediction
by: Bochao Sun, et al.
Published: (2023-12-01)

A clinical prediction model based on interpretable machine learning algorithms for prolonged hospital stay in acute ischemic stroke patients: a real-world study
by: Kai Wang, et al.
Published: (2023-11-01)

Accelerating the discovery of high-performance donor/acceptor pairs in photovoltaic materials via machine learning and density functional theory
by: Xiujuan Liu, et al.
Published: (2022-04-01)

Explainable Machine Learning-Based Risk Prediction Model for In-Hospital Mortality after Continuous Renal Replacement Therapy Initiation
by: Pei-Shan Hung, et al.
Published: (2022-06-01)

XML-CIMT: Explainable Machine Learning (XML) Model for Predicting Chemical-Induced Mitochondrial Toxicity
by: Keerthana Jaganathan, et al.
Published: (2022-12-01)

Machine Learning for the Prediction of Red Blood Cell Transfusion in Patients During or After Liver Transplantation Surgery
by: Le-Ping Liu, et al.
Published: (2021-02-01)

Identifying key multi-modal predictors of incipient dementia in Parkinson’s disease: a machine learning analysis and Tree SHAP interpretation
by: G. Peggy McFall, et al.
Published: (2023-06-01)

Physics‐Based Human‐in‐the‐Loop Machine Learning Combined with Genetic Algorithm Search for Multicriteria Optimization: Electrochemical CO2 Reduction Reaction
by: Naohiro Fujinuma, et al.
Published: (2023-05-01)

Explaining Deep Q-Learning Experience Replay with SHapley Additive exPlanations
by: Robert S. Sullivan, et al.
Published: (2023-10-01)

Predicting Depression during the COVID-19 Pandemic Using Interpretable TabNet: A Case Study in South Korea
by: Hung Viet Nguyen, et al.
Published: (2023-07-01)

Prediction of Aircraft Go-Around during Wind Shear Using the Dynamic Ensemble Selection Framework and Pilot Reports
by: Afaq Khattak, et al.
Published: (2022-12-01)

Prediction and Interpretation of Low-Level Wind Shear Criticality Based on Its Altitude above Runway Level: Application of Bayesian Optimization–Ensemble Learning Classifiers and SHapley Additive exPlanations
by: Afaq Khattak, et al.
Published: (2022-12-01)

SHAP and LIME: An Evaluation of Discriminative Power in Credit Risk
by: Alex Gramegna, et al.
Published: (2021-09-01)

Artificial intelligence annotated clinical-pathologic risk model to predict outcomes of advanced gastric cancer
by: Yan Chen, et al.
Published: (2023-03-01)

Characteristics of fatal occupational injuries in migrant workers in South Korea: A machine learning study
by: Ju-Yeun Lee, et al.
Published: (2023-09-01)

Machine Learning Algorithms Associate Case Numbers with SARS-CoV-2 Variants Rather Than with Impactful Mutations
by: Matthieu Vilain, et al.
Published: (2023-05-01)

Exploring the predictors affecting the sense of community of Korean high school students: application of random forests and SHAP
by: Eunah Jang, et al.
Published: (2024-02-01)

Dynamic Vegetation Responses to Climate and Land Use Changes over the Inner Mongolia Reach of the Yellow River Basin, China
by: Tingxiang Liu, et al.
Published: (2023-07-01)

Machine Learning Models Using SHapley Additive exPlanation for Fire Risk Assessment Mode and Effects Analysis of Stadiums
by: Ying Lu, et al.
Published: (2023-02-01)

Predicting Critical Path of Labor Dispute Resolution in Legal Domain by Machine Learning Models Based on SHapley Additive exPlanations and Soft Voting Strategy
by: Jianhua Guan, et al.
Published: (2024-01-01)

Toward Better Risk Stratification for Implantable Cardioverter-Defibrillator Recipients: Implications of Explainable Machine Learning Models
by: Yu Deng, et al.
Published: (2022-09-01)

Integrated intelligent models for predicting water pipe failure probability
by: Ridwan Taiwo, et al.
Published: (2024-01-01)