Development, deployment, and feature interpretability of a three-class prediction model for pulmonary diseases

Development, deployment, and feature interpretability of a three-class prediction model for pulmonary diseases

Abstract Purpose To develop a high-performance machine learning model for predicting and interpreting features of pulmonary diseases. Patients and methods This retrospective study analyzed clinical and imaging data from patients with non-small cell lung cancer (NSCLC), granulomatous inflammation, an...

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Bibliographic Details
Main Authors: Zhenyu Cao, Gang Xu, Yuan Gao, Jianying Xu, Fengjuan Tian, Hengfeng Shi, Dengfa Yang, Zongyu Xie, Jian Wang
Format: Article
Language:English
Published: SpringerOpen 2025-06-01
Series:Insights into Imaging
Subjects:
Machine learning
SHAP
Rimmed sign
Satellite lesion
Online Access:https://doi.org/10.1186/s13244-025-02020-7
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https://doi.org/10.1186/s13244-025-02020-7

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