AIMC Topic: Risk Factors

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Predicting pregnancy-related pelvic girdle pain using machine learning.

Musculoskeletal science & practice
BACKGROUND: Pregnancy-related pelvic girdle pain (PPGP) is a common complication during gestation which negatively influences pregnant women's quality of life. There are numerous risk factors associated with PPGP, however, there is limited informatio...

Machine learning-based prediction of hearing loss: Findings of the US NHANES from 2003 to 2018.

Hearing research
The prevalence of hearing loss (HL) has emerged as an escalating public health concern globally. The objective of this study was to leverage data from the National Health and Nutritional Examination Survey (NHANES) to develop an interpretable predict...

Machine learning models based on a national-scale cohort accurately identify patients at high risk of deep vein thrombosis following primary total hip arthroplasty.

Orthopaedics & traumatology, surgery & research : OTSR
BACKGROUND: The occurrence of deep venous thrombosis (DVT) following total hip arthroplasty (THA) poses a substantial risk of morbidity and mortality, highlighting the need for preoperative risk stratification and prophylaxis initiatives. However, th...

Optimizing stability of heart disease prediction across imbalanced learning with interpretable Grow Network.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Heart disease prediction models often face stability challenges when applied to public datasets due to significant class imbalances, unlike the more balanced benchmark datasets. These imbalances can adversely affect various...

OptiStack classifier: optimized stacking framework with ensemble feature engineering for enhanced cardiovascular risk prediction.

Inflammation research : official journal of the European Histamine Research Society ... [et al.]
BACKGROUND: Cardiovascular diseases (CVD) are a leading cause of morbidity and mortality globally, highlighting the urgent need for accurate risk prediction to improve early intervention and management. Traditional models have difficulty capturing th...

Comparison of machine learning models for predicting stroke risk in hypertensive patients: Lasso regression model, random forest model, Boruta algorithm model, and Boruta algorithm combined with Lasso regression model.

Medicine
The aim of this study was to compare the performance of 4 machine learning models-Lasso regression model, random forest model, Boruta algorithm model, and the Boruta algorithm combined with Lasso regression-in predicting stroke risk among hypertensiv...

Application of machine learning algorithms in osteoporosis analysis based on cardiovascular health assessed by life's essential 8: a cross-sectional study.

Journal of health, population, and nutrition
BACKGROUND: Life's Essential 8 (LE8) for assessing cardiovascular health (CVH) has been demonstrated to be inversely associated with osteoporosis (OP). This study aims to create a machine learning (ML) model to assess the clinical association value o...