Support vector machines improve classification by mapping inseparable signals into higher-dimensional spaces. Random forest models, through ensemble decision trees, increase robustness against ...
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 ...
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 ...
Abstract: The risk of pedestrian-involved traffic accidents represents a significant challenge to road safety and necessitates objective methods for analyzing the contributing factors. This study ...
Heat stress is widely recognized as a critical risk factor in livestock systems. Rising temperatures and humidity levels can ...
Machine learning enhances proteomics by optimizing peptide identification, structure prediction, and biomarker discovery.
Abstract: Magnetic localization does not require line of sight, this makes it suitable for various applications, including indoor navigation, surgical tracking, motion capture, and 3D body scanning.
A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ...
“Tooth agenesis, a congenital condition characterized by the absence of one or more teeth, is among the most common and ...
Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk ...
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