AIMC Topic: Prevalence
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Machine learning evaluation of a hypertension screening program in a university workforce over five years.
Scientific reports
Dec 4, 2024
The global prevalence of hypertension continues excessively elevated, especially among low- and middle-income nations. Workplaces provide tremendous opportunities as a unique, easily accessible and practical avenue for early diagnosis and treatment o...
Exploring the risk factors and clustering patterns of periodontitis in patients with different subtypes of diabetes through machine learning and cluster analysis.
Acta odontologica Scandinavica
Dec 3, 2024
AIM: To analyse the risk factors contributing to the prevalence of periodontitis among clusters of patients with diabetes and to examine the clustering patterns of clinical blood biochemical indicators.
Hearing loss prediction equation for Iranian truck drivers using neural network algorithm.
Work (Reading, Mass.)
Nov 25, 2024
BACKGROUND:  Given the high prevalence of hearing loss among truck drivers, using artificial neural networks (ANNs) to predict and detect contributing factors early can aid managers significantly.
Artificial intelligence-based body composition analysis using computed tomography images predicts both prevalence and incidence of diabetes mellitus.
Journal of diabetes investigation
Nov 22, 2024
AIM/INTRODUCTION: We assess the efficacy of artificial intelligence (AI)-based, fully automated, volumetric body composition metrics in predicting the risk of diabetes.
Application of Machine Learning and Deep Neural Visual Features for Predicting Adult Obesity Prevalence in Missouri.
International journal of environmental research and public health
Nov 19, 2024
This research study investigates and predicts the obesity prevalence in Missouri, utilizing deep neural visual features extracted from medium-resolution satellite imagery (Sentinel-2). By applying a deep convolutional neural network (DCNN), the study...
Risk of crashes among self-employed truck drivers: Prevalence evaluation using fatigue data and machine learning prediction models.
Journal of safety research
Nov 16, 2024
INTRODUCTION: Transportation companies have increasingly shifted their workforce from permanent to outsourced roles, a trend that has consequences for self-employed truck drivers. This transition leads to extended working hours, resulting in fatigue ...
Development of a machine learning-based risk assessment model for loneliness among elderly Chinese: a cross-sectional study based on Chinese longitudinal healthy longevity survey.
BMC geriatrics
Nov 14, 2024
BACKGROUND: Loneliness is prevalent among the elderly and has intensified due to global aging trends. It adversely affects both mental and physical health. Traditional scales for measuring loneliness may yield biased results due to varying definition...
Unlocking insights: Using machine learning to identify wasting and risk factors in Egyptian children under 5.
Nutrition (Burbank, Los Angeles County, Calif.)
Nov 12, 2024
INTRODUCTION: Malnutrition, particularly wasting, continues to be a significant public health issue among children under five years in Egypt. Despite global advancements in child health, the prevalence of wasting remains a critical concern. This stud...
Carcinogenic and non-carcinogenic risks caused by rice contamination with heavy metals and their effect on the prevalence of cardiovascular disease (Using machine learning).
Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association
Nov 7, 2024
INTRODUCTION: The safety and health of food products are essential in the food industry, and the risk of contamination from various contaminants must be evaluated. Exposure to HMs from the environment (especially food) causes various adverse effects ...
Trends in the prevalence of osteoporosis and effects of heavy metal exposure using interpretable machine learning.
Ecotoxicology and environmental safety
Oct 28, 2024
There is limited evidence that heavy metals exposure contributes to osteoporosis. Multi-parameter scoring machine learning (ML) techniques were developed using National Health and Nutrition Examination Survey data to predict osteoporosis based on hea...
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