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

The association between metabolic risk factors, nonalcoholic fatty liver disease, and the incidence of liver cancer: a nationwide population-based cohort study

(2022) Yu-Guang Chen et al.   Hepatology International

Machine learning-aided risk prediction for metabolic syndrome based on 3 years study

(2022) Haizhen Yang et al.   Scientific Reports

Prediction of metabolic and pre-metabolic syndromes using machine learning models with anthropometric, lifestyle, and biochemical factors from a middle-aged population in Korea

(2022) Junho Kim et al.   BMC PUBLIC HEALTH

Early prediction of incident liver disease using conventional risk factors and gut-microbiome-augmented gradient boosting

(2022) Yang Liu et al.   Cell Metabolism

Lupus nephritis diagnosis using enhanced moth flame algorithm with support vector machines

(2022) Mingjing Wang et al.   COMPUTERS IN BIOLOGY AND MEDICINE

Machine learning versus logistic regression for prognostic modelling in individuals with non-specific neck pain

(2022) Bernard X. W. Liew et al.   EUROPEAN SPINE JOURNAL

Association between Early Life Famine Exposure and Metabolic Syndrome in Adulthood

(2022) Fan Yao et al.   Nutrients

Classification of Breast Tumors Based on Histopathology Images Using Deep Features and Ensemble of Gradient Boosting Methods

(2022) Mohammad Reza Abbasniya et al.   COMPUTERS & ELECTRICAL ENGINEERING

Prediction of metabolic syndrome: A machine learning approach to help primary prevention

(2022) Leonardo Daniel Tavares et al.   DIABETES RESEARCH AND CLINICAL PRACTICE

Opening the black box: interpretable machine learning for predictor finding of metabolic syndrome

(2022) Yan Zhang et al.   BMC Endocrine Disorders

Detection and Visualisation of Pneumoconiosis Using an Ensemble of Multi-Dimensional Deep Features Learned from Chest X-rays

(2022) Liton Devnath et al.   International Journal of Environmental Research and Public Health

COVID-19 and metabolic diseases: a heightened awareness of health inequities and a renewed focus for research priorities

(2021) William T. Cefalu et al.   Cell Metabolism

Progresses in Food Packaging, Food Quality, and Safety—Controlled-Release Antioxidant and/or Antimicrobial Packaging

(2021) Cornelia Vasile et al.   MOLECULES

Induction of decision trees as classification models through metaheuristics

(2021) Rafael Rivera-Lopez et al.   Swarm and Evolutionary Computation

Metabolic Impacts of Confinement during the COVID-19 Pandemic Due to Modified Diet and Physical Activity Habits

(2020) María Martinez-Ferran et al.   Nutrients

Prediction of Metabolic Syndrome in a Mexican Population Applying Machine Learning Algorithms

(2020) Guadalupe Obdulia Gutiérrez-Esparza et al.   Symmetry-Basel

Machine learning for metabolic engineering: A review

(2020) Christopher E. Lawson et al.   METABOLIC ENGINEERING

Identification of Traditional Chinese Medicine Constitutions and Physiological Indexes Risk Factors in Metabolic Syndrome: A Data Mining Approach

(2019) Yanchao Tang et al.   Evidence-based Complementary and Alternative Medicine

Associations of Nutrient Patterns with the Prevalence of Metabolic Syndrome: Results from the Baseline Data of the Japan Multi-Institutional Collaborative Cohort Study

(2019) Iwasaki et al.   Nutrients

Identification of female-specific genetic variants for metabolic syndrome and its component traits to improve the prediction of metabolic syndrome in females

(2019) Sokanha Kong et al.   BMC Medical Genetics

Risk models and scores for metabolic syndrome: systematic review protocol

(2019) Musa Saulawa Ibrahim et al.   BMJ Open

Nutritional Challenges in Metabolic Syndrome

(2019) Irene Hoyas et al.   Journal of Clinical Medicine

Prediction models to identify individuals at risk of metabolic syndrome who are unlikely to participate in a health intervention program

(2018) Akihiro Shimoda et al.   INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS

Synthetic minority oversampling technique for multiclass imbalance problems

(2017) Tuanfei Zhu et al.   PATTERN RECOGNITION

Particle swarm optimization (PSO). A tutorial

(2015) Federico Marini et al.   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

Predicting Metabolic Syndrome Using the Random Forest Method

(2015) Apilak Worachartcheewan et al.   TheScientificWorldJOURNAL

Predicting Metabolic Syndrome Using the Random Forest Method

(2015) Apilak Worachartcheewan et al.   Scientific World Journal

A comprehensive review of firefly algorithms

(2013) Iztok Fister et al.   Swarm and Evolutionary Computation

Epidemiology of Obesity, the Metabolic Syndrome, and Chronic Kidney Disease

(2012) Rikki M. Tanner et al.   CURRENT HYPERTENSION REPORTS

Literature review regarding Ant Colony Optimization applied to scheduling problems: Guidelines for implementation and directions for future research

(2012) R.F. Tavares Neto et al.   ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

Relation of aging and sex hormones to metabolic syndrome and cardiovascular disease

(2011) Verónica Guarner-Lans et al.   EXPERIMENTAL GERONTOLOGY

Association of white blood cell count with metabolic syndrome in patients undergoing peritoneal dialysis

(2009) Jung Tak Park et al.   METABOLISM-CLINICAL AND EXPERIMENTAL