AIMC Topic: Body Mass Index
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Exploring the triglyceride-glucose index's role in depression and cognitive dysfunction: Evidence from NHANES with machine learning support.
Journal of affective disorders
Jan 11, 2025
BACKGROUND: Depression and cognitive impairments are prevalent among older adults, with evidence suggesting potential links to obesity and lipid metabolism disturbances. This study investigates the relationships between the triglyceride-glucose (TyG)...
Machine learning-based analyses of contributing factors for the development of hypertension: a comparative study.
Clinical and experimental hypertension (New York, N.Y. : 1993)
Jan 8, 2025
OBJECTIVES: Sufficient attention has not been given to machine learning (ML) models using longitudinal data for investigating important predictors of new onset of hypertension. We investigated the predictive ability of several ML models for the devel...
Multimodal machine learning for analysing multifactorial causes of disease-The case of childhood overweight and obesity in Mexico.
Frontiers in public health
Jan 7, 2025
BACKGROUND: Mexico has one of the highest global incidences of paediatric overweight and obesity. Public health interventions have shown only moderate success, possibly from relying on knowledge extracted using limited types of statistical data analy...
Using Machine Learning to Predict Weight Gain in Adults: an Observational Analysis From the All of Us Research Program.
The Journal of surgical research
Dec 31, 2024
INTRODUCTION: Obesity, defined as a body mass index ≥30 kg/m, is a major public health concern in the United States. Preventative approaches are essential, but they are limited by an inability to accurately predict individuals at highest risk of weig...
Integrating Remote Photoplethysmography and Machine Learning on Multimodal Dataset for Noninvasive Heart Rate Monitoring.
Sensors (Basel, Switzerland)
Nov 26, 2024
Non-contact heart monitoring is crucial in advancing telemedicine, fitness tracking, and mass screening. Remote photoplethysmography (rPPG) is a non-contact technique to obtain information about heart pulse by analyzing the changes in the light inten...
Next-visit prediction and prevention of hypertension using large-scale routine health checkup data.
PloS one
Nov 13, 2024
This paper proposes the use of machine learning models to predict one's risk of having hypertension in the future using their routine health checkup data of their current and past visits to a health checkup center. The large-scale and high-dimensiona...
Predictive modeling of gestational weight gain: a machine learning multiclass classification study.
BMC pregnancy and childbirth
Nov 8, 2024
BACKGROUND: Gestational weight gain (GWG) is a critical factor influencing maternal and fetal health. Excessive or insufficient GWG can lead to various complications, including gestational diabetes, hypertension, cesarean delivery, low birth weight, ...
Physiological model-based machine learning for classifying patients with binge-eating disorder (BED) from the Oral Glucose Tolerance Test (OGTT) curve.
Computer methods and programs in biomedicine
Oct 31, 2024
BACKGROUND AND OBJECTIVE: Binge eating disorder (BED) is the most frequent eating disorder, often confused with obesity, with which it shares several characteristics. Early identification could enable targeted therapeutic interventions. In this study...
Modeling health risks using neural network ensembles.
PloS one
Oct 9, 2024
This study aims to demonstrate that demographics combined with biometrics can be used to predict obesity related chronic disease risk and produce a health risk score that outperforms body mass index (BMI)-the most commonly used biomarker for obesity....
Machine-Learning Application for Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease Using Laboratory and Body Composition Indicators.
Archives of Iranian medicine
Oct 1, 2024
BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a significant global health burden without established curative therapies. Early detection and preventive strategies are crucial for effective MASLD management. T...
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