AIMC Topic: Machine Learning

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Interpretable machine learning for cardiovascular risk prediction: Insights from NHANES dietary and health data.

PloS one
BACKGROUND: Cardiovascular diseases (CVD) are one of the leading global causes of death, which requires an accurate early prediction. This study aimed to develop transparent machine learning (ML) models using National Health and Nutrition Examination...

Machine learning based fault classification for improved induction motor performance.

PloS one
This study explores the design of an effective fault classification algorithm for 3 phase induction motor, an integral unit in many industrial systems. It is found that traditional fault detection methods and deep learning approaches are both effecti...

Impact of blood culture positivity at intensive care unit admission on mortality in infective endocarditis: Machine learning and deep learning-based causal inference models.

PloS one
BACKGROUND: Infective endocarditis (IE) carries high in-hospital mortality, particularly among intensive care unit (ICU) patients. The predictive role of blood culture positivity in these patients remains unclear.

Mortality risk prediction in NSTE-ACS following PCI: Insights from a real-world cohort.

PloS one
BACKGROUND: Non-ST-segment elevation acute coronary syndrome (NSTE-ACS) is a major contributor to cardiovascular mortality, yet reliable tools for individualized mortality prediction remain limited. Machine learning offers the potential to enhance pr...

Towards better Hebrew clickbait detection: Insights from BERT and data augmentation.

PloS one
Clickbait headlines, designed to entice readers with sensationalized or misleading content, pose significant challenges in the digital landscape. They exploit curiosity to generate traffic and revenue, often at the cost of spreading misinformation an...

Enhanced random forest with geologically-informed feature optimization for complex volcanic rock lithology identification: A case study in the Wangfu Fault Depression, Songliao Basin.

PloS one
Identifying lithologies within the volcanic reservoirs of the Huoshiling Formation (Wangfu Fault Depression, Songliao Basin) remains challenging due to extreme heterogeneity, limited core control, and ambiguous responses on conventional logs. We intr...

Storage life prediction and quality discrimination of instant green tea: Integrating computer vision, electronic nose, and electronic tongue.

Food chemistry
Tea storage is a critical determinant in determining the quality of tea products. This study systematically investigated the quality alterations of instant green tea during storage and developed an intelligent evaluation method by integrating compute...

Integrating multi-omics and machine learning to decipher the role of GSTP1 in endocrine-disrupting chemical-induced prostate cancer pathogenesis.

European journal of pharmacology
Prostate cancer (PCa) pathogenesis involves complex interactions between genetic susceptibility and exposure to endocrine-disrupting chemicals (EDCs). This study aimed to systematically identify key genes linking EDC exposure to PCa using an integrat...

Evaluation of model performance in predicting sepsis after intestinal obstruction surgery: a multicenter retrospective study.

Annals of medicine
PURPOSE: Intestinal obstruction surgery is a high-risk procedure associated with postoperative sepsis. In this multicenter retrospective study, we aimed to employ machine-learning methods to predict sepsis after intestinal obstruction surgery and vis...

Machine Learning-Based Bioactivity Prediction and Descriptor-Guided Rational Design of Amyloid-β Aggregation Inhibitors.

ACS chemical neuroscience
Alzheimer's disease (AD) is a progressive neurodegenerative disorder in which amyloid-β (Aβ) aggregation plays a pivotal role in its onset and progression. Inhibiting Aβ aggregation is a promising therapeutic strategy; however, its intrinsically diso...