Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention and management strategies. Traditional risk ...

Researchers developed and validated ElasticNet machine learning models that predict 12-month MMSE and BADL outcomes in ...

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 ...

Chronic kidney disease (CKD) constitutes a major global health challenge, affecting millions and often remaining undiagnosed until advanced stages. Recent advances in machine learning have ...

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered considerable interest among researchers. The debate around the use of machine ...

FIU Researchers are training AI to detect heart conditions, like aortic stenosis and heart failure, by analyzing heart sound data to improve early diagnosis and risk prediction. The future of heart ...

Discover how AI is transforming nutritional science by turning complex diet and omics data into predictive tools that reshape chronic disease prevention and personalized care.

Researchers from Odisha and Saudi Arabia have developed a hybrid AI model achieving 95.49% accuracy in predicting liver disease. This innovative tool, combining deep learning and boosting, promises ...

Cancer, Alzheimer’s, and other diseases follow a pathway in the human body. It starts at the molecular and cellular levels, and through a series of complex interactions can lead to the development and ...

Machine learning enhances proteomics by optimizing peptide identification, structure prediction, and biomarker discovery.