Both approaches identified hemoglobin as one of the most significant predictors of CKD risk. Additional top-ranked features included blood urea, sodium levels, red blood cell count, potassium, and ...
Machine learning enhances proteomics by optimizing peptide identification, structure prediction, and biomarker discovery.
Sensors, computer vision models, and artificial intelligence have combined to help CEAT Tyres’ Chennai factory reduce defects, waste and energy use, a.
A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ...
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Study shows sharp oasis decline in southeastern Morocco’s Ternata over the past 40 years
A group of eight researchers has pointed to a steady decline in the Ternata Oasis, southeastern Morocco, over the past 40 ...
Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention and management strategies. Traditional risk ...
Researchers used 16S rRNA sequencing and machine learning to identify gut microbiome patterns associated with insulin resistance severity in people with type 2 diabetes. XGBoost models showed that ...
As Raipur expands, the Kharun River Basin faces intensifying floods and sediment loads. Explore how climate change and land-use shifts are erasing the predictability of India’s monsoon heartland.
Abstract: The recent development of advanced data analytics and machine learning promoted the introduction of diverse learning techniques designed to alleviate challenges related to two major ...
RESEARCHERS reported that new transcranial magnetic stimulation (TMS) biomarkers, combined with machine learning, accurately distinguished individuals with major depressive disorder (MDD) from healthy ...
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