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 ...

A new risk prediction model shows good predictive value in identifying risk for neurogenic bladder (NB) after spinal cord injury (SCI) and guiding clinical interventions, according to a study ...

Approximately one in seven adults in the United States has kidney disease, where the organs responsible for filtering waste ...

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 ...

10 The George Institute for Global Health, School of Public Health, Imperial College London, London, UK Background Cardiovascular risk is underassessed in women. Many women undergo screening ...

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 ...

A powerful artificial intelligence (AI) tool could give clinicians a head start in identifying life-threatening complications ...

AI catches connections we miss. AI-IR was trained using independent cohorts from the United States and Taiwan, a collection of anonymized medical data ...

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...

This repository consist of various machine learning models along with the dataset. The models are trained with widely used ML algorithms like Gradient Boost , Random Forest etc. Pickle is used to ...