An accurate assessment of the state of health (SOH) is the cornerstone for guaranteeing the long-term stable operation of ...

Abstract: In this paper, the prediction of Olympic medal winners has been explored using various machine learning models, utilizing a dataset spanning 128 years of Olympic history from 1896 to 2024.

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.

Cervical spondylotic myelopathy (CSM) refers to spinal cord compression from arthritis in the neck and is the leading cause ...

Read more about AI-driven air quality system promises faster, more reliable urban health warnings on Devdiscourse ...

New deep-learning framework reconstructs hourly PM2.5 chemical composition using air-quality and meteorological data ...

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 ...

Tree-based ensemble models often outperform more complex deep learning architectures when applied to structured, tabular IoT data. While neural networks excel with image and unstructured inputs, ...

Objective To develop and validate an interpretable machine learning (ML)-based frailty risk prediction model that combines real-time health data with validated scale assessments for enhanced ...