A team of researchers at the Icahn School of Medicine at Mount Sinai has developed a new method to identify and reduce biases ...

Background Machine learning based on clinical characteristics has the potential to predict coronary CT angiography (CCTA) findings and help guide resource utilisation.Methods From the SCOT-HEART ...

Demographic bias gaps are closing in face recognition, but how training images are sourced is becoming the field’s biggest privacy fight.

Previous studies using machine learning techniques using similar models to diagnose migraines using support vector machine, random forest, and artificial neural networks (21 – 24) have yielded ...

This paper presents a comprehensive machine learning approach for credit score classification, addressing key challenges in financial risk assessment. We propose an optimized CatBoost-based framework ...

Abstract Depressive disorders are complex, multifactorial conditions that exhibit significant variability in treatment response, often influenced by gender differences. This study leverages advanced ...

At the same time, four machine learning (ML)-based algorithms like support vector machine (SVM), random forest, AdaBoost, and Gradient Boosting (GB) were employed for gender classification. We trained ...

Artificial intelligence (AI) computer programs that process MRI results show differences in how the brains of men and women are organized at a cellular level, a new study shows.