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

ABSTRACT:

Introduction

Interstitial lung disease (ILD) comprises various disorders marked by pulmonary inflammation and fibrosis. Early diagnosis and risk prediction are vital for improving patient outcomes.

Methods

We retrospectively analyzed 603 patients who had visited the Hubin Campus between January 2022 and April 2025, employing a 1:2 case-control design with age- and gender-matched groups. We collected clinical information, complete blood count data, lipid metabolism indicators, and various derived indices.

Conclusion

Six key markers were identified through three machine learning algorithms (LassoCV, SVMREFCV, and Boruta): neutrophil percentage, lymphocyte percentage, monocyte percentage, hemoglobin, and two novel ratios - neutrophil-to-HDL-C and lymphocyte-to-HDL-C. The

SUBMITTER: Zhu L 

PROVIDER: S-EPMC12545033 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature