INTRODUCTION: Dyslipidemia as a modifiable risk factor for chronic non-communicable diseases has become a worldwide concern. We aim to explore different anthropometric measures as predictors of dyslipidemia using various machine learning methods.
Applied and environmental microbiology
Aug 14, 2025
comprise ecologically significant bacteria that thrive in warm, moderately saline water, and their incidence and proliferation are strongly influenced by environmental factors. In recent years, . infections have been reported more frequently and ove...
Malaria remains a significant global health concern, contributing to substantial morbidity and mortality worldwide. To inform efforts aimed at alleviating the global malaria burden, this study utilized spatial analysis, advanced machine learning (ML)...
A growing body of literature supports the association between ambient particulate pollution and the risk of type 2 diabetes (T2DM). Both issues are particularly relevant in Italy. This study investigates the relationship between T2DM and exposure to ...
OBJECTIVES: Pancreatitis is common following endoscopic retrograde cholangiopancreatography (ERCP). Despite increased vigilance of post-ERCP pancreatitis (PEP), both its incidence and associated mortality are rising. Risk prediction models may provid...
BACKGROUND: Limited research has been conducted on the prevalence of acute kidney injury (AKI) and acute kidney disease (AKD) in gout patients, as well as the impact of these renal complications on patient outcomes. This study aims to develop machine...
INTRODUCTION: Accurate breast cancer risk prediction is essential for early detection and personalized prevention strategies. While traditional models, such as Gail and Tyrer-Cuzick, are widely utilized, machine learning-based approaches may offer en...
Bone metastasis (BM) is common in high-grade lung neuroendocrine tumors (NETs). This study aimed to use multiple machine learning algorithms to exploring the significant factors associated with synchronous BM in these patients. Patients diagnosed wit...
In this study, we investigated the correlation between air pollution indicators and pulmonary tuberculosis (TB) incidence and mortality rates across provincial administrative regions of China from January 2013 to December 2020 to develop predictive m...
OBJECTIVE: The aim of our study is to determine the main predictors of postoperative AKI in neonates using machine learning models compared with the logistic regression model.
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