In a 2025 study, researchers led by Yuqi Yang introduced a ten-feature random forest model to predict MASLD with high accuracy. By comparing traditional indices with digital analysis, they highlighted key predictors like waist-height ratio and fasting glucose. This work offers a promising, data-driven approach for early clinical diagnosis and better health management.