Risk Factor

Risk factor research aims to identify and quantify factors contributing to various health outcomes and adverse events, enabling better prediction, prevention, and intervention strategies. Current research heavily utilizes machine learning, employing algorithms like random forests, support vector machines, and deep learning models (including convolutional neural networks and recurrent neural networks) to analyze diverse data sources, such as electronic health records, imaging data, and even social media. This work is significant because it facilitates more accurate risk assessments across numerous diseases and conditions, from cardiovascular disease and diabetes to mental health disorders and even femicide, ultimately improving healthcare and public health outcomes. The development of explainable AI models is a growing trend, enhancing the transparency and clinical utility of risk prediction tools.