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 ...

Objective This study reviewed the current state of machine learning (ML) research for the prediction of sports-related injuries. It aimed to chart the various approaches used and assess their efficacy ...

Postpartum depression (PPD) affects up to 15 percent of individuals after childbirth. Early identification of patients at risk of PPD could improve proactive mental health support. Researchers ...

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

MASLD is prevalent in T2DM patients, with a 65% occurrence rate, and poses a higher risk for severe liver diseases. The study analyzed 3,836 T2DM patients, identifying key predictors like BMI, ...

A machine learning model predicted cardiac tamponade during AF ablation with high accuracy. Learn how XGBoost may improve risk stratification.

Researchers led by Xian-Yang Qin at the RIKEN Center for Integrative Medical Sciences (IMS) in Japan have developed a score that predicts the risk of liver cancer. Published in the scientific journal ...

Sensor data from wearable devices analyzed over five years reveals walking and posture differences that predict fall risk in Parkinson’s patients. Study: Predicting future fallers in Parkinson’s ...

An interdisciplinary team of researchers has developed a machine learning framework that uses limited water quality samples to predict which inorganic pollutants are likely to be present in a ...