AIMC Topic: Nutrition Surveys

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The impact of multipollutant exposure on hepatic steatosis: a machine learning-based investigation into multipollutant synergistic effects.

Frontiers in public health
INTRODUCTION: This study examines the synergistic effects of multi-pollutant exposure on hepatic lipid accumulation in non-alcoholic fatty liver disease (NAFLD) through the application of an explainable machine learning framework. This approach addre...

Is there a competitive advantage to using multivariate statistical or machine learning methods over the Bross formula in the hdPS framework for bias and variance estimation?

PloS one
PURPOSE: We aim to evaluate various proxy selection methods within the context of high-dimensional propensity score (hdPS) analysis. This study aimed to systematically evaluate and compare the performance of traditional statistical methods and machin...

Application of machine learning algorithms in predicting new onset hypertension: a study based on the China Health and Nutrition Survey.

Environmental health and preventive medicine
BACKGROUND: Hypertension is a serious chronic disease that can significantly lead to various cardiovascular diseases, affecting vital organs such as the heart, brain, and kidneys. Our goal is to predict the risk of new onset hypertension using machin...

Machine learning-driven risk assessment of coronary heart disease: Analysis of NHANES data from 1999 to 2018.

Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences
OBJECTIVES: The high incidence of coronary artery heart disease (CHD) poses a significant burden and challenge to public health systems globally. Effective prevention and early diagnosis of CHD have become key strategies to alleviate this burden. Thi...

Comparative analysis of machine learning models for efficient low back pain prediction using demographic and lifestyle factors.

Journal of back and musculoskeletal rehabilitation
BACKGROUND: Low back pain (LBP) is one of the most frequently occurring musculoskeletal disorders, and factors such as lifestyle as well as individual characteristics are associated with LBP.

Coronary heart disease classification using deep learning approach with feature selection for improved accuracy.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Coronary heart disease (CHD) is one of the deadliest diseases and a risk prediction model for cardiovascular conditions is needed. Due to the huge number of features that lead to heart problems, it is often difficult for an expert to eval...

Machine Learning-Based Prediction of Elevated PTH Levels Among the US General Population.

The Journal of clinical endocrinology and metabolism
CONTEXT: Although elevated parathyroid hormone (PTH) levels are associated with higher mortality risks, the evidence is limited as to when PTH is expected to be elevated and thus should be measured among the general population.

Machine Learning Model for Predicting CVD Risk on NHANES Data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Cardiovascular disease (CVD) is a major health problem throughout the world. It is the leading cause of morbidity and mortality and also causes considerable economic burden to society. The early symptoms related to previous observations and abnormal ...

Reporting of demographic data and representativeness in machine learning models using electronic health records.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: The development of machine learning (ML) algorithms to address a variety of issues faced in clinical practice has increased rapidly. However, questions have arisen regarding biases in their development that can affect their applicability i...