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.

A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ...

Sensors, computer vision models, and artificial intelligence have combined to help CEAT Tyres’ Chennai factory reduce defects, waste and energy use, a.

A machine learning model predicted cardiac tamponade during AF ablation with high accuracy. Learn how XGBoost may improve risk stratification.

Background Patients with heart failure (HF) frequently suffer from undetected declines in cardiorespiratory fitness (CRF), which significantly increases their risk of poor outcomes. However, current ...

A group of eight researchers has pointed to a steady decline in the Ternata Oasis, southeastern Morocco, over the past 40 ...

By Hugo Francisco de Souza A new study shows that gut microbiome signatures, analyzed through advanced machine learning, can help identify individuals with more severe insulin resistance, offering ...

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.

Read more about From disease detection to biomass forecasting: AI improves aquaculture risk strategy on Devdiscourse ...