Identification of routine blood derived hematological and lipid indices in ILD through machine learning; a retrospective case-control study

Based on these findings, we developed an online prediction tool to assist primary care clinicians in assessing the risk of ILD in suspected cases. Our results indicate that the random forest model exhibits high accuracy and clinical utility for early ILD prediction, providing a novel tool and methodology for early diagnosis and intervention. Future studies will focus on further optimizing the model and validating it in larger multicenter cohorts.