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 ...
Data is the life-blood of physical AI. Collecting real-life data is expensive. Generative AI and diffusion to create ...
Machine learning enhances proteomics by optimizing peptide identification, structure prediction, and biomarker discovery.
Cervical spondylotic myelopathy (CSM) refers to spinal cord compression from arthritis in the neck and is the leading cause of spinal cord dysfunction in older adults. CSM is a chronic, progressive ...
Machine learning has rapidly become integral to the advancement of geoscience, a field inundated with complex and multivariate data from myriad sources such ...
Read more about AI-driven air quality system promises faster, more reliable urban health warnings on Devdiscourse ...
Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention and management strategies. Traditional risk ...
Data Normalization vs. Standardization is one of the most foundational yet often misunderstood topics in machine learning and ...
BACKGROUND: Mental stress-induced myocardial ischemia is often clinically silent and associated with increased cardiovascular risk, particularly in women. Conventional ECG-based detection is limited, ...
Optokinetic Nystagmus (OKN) is a natural reflexive eye movement in oculomotor studies, reflecting the health status of the visual system. Through accurate eye center annotation, physicians can observe ...
Abstract: To address the issues of slow convergence and low exploration efficiency in traditional Q-learning, this paper proposes a path planning algorithm based on support vector machine (SVM) ...
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