Journal of Medical Internet Research

Explainable Machine Learning Model for Predicting Persistent Sepsis-Associated Acute Kidney Injury: Development and Validation Study

Early identification and prediction of persistent SA-AKI are crucial. Objective: The aim of this study was to develop and validate an interpretable machine learning (ML) model that predicts persistent ...
IEEE

PCASTNet: A Physics-Constrained Adaptive Style Transfer Network for Sample Generation in Cross-Machine Small-Sample Fault Diagnosis

Abstract: To compensate for the overlooked data realism and physical consistency in traditional cross-machine sample generation methods, this article proposes a physics-constrained adaptive style ...
IEEE

Predicting China's Marriage Rate using Machine Learning Models and Cross-Validation Evaluation

Abstract: After China's accession to the WTO, the country has experienced rapid development over the past 20 years, but the marriage rate has been declining. This study aims to analyze marriage data ...
Frontiers

Development and validation of explainable machine learning models for predicting 3-month functional outcomes in acute ischemic stroke: a SHAP-based approach

1 Department of Neurology, The Affiliated Yixing Hospital of Jiangsu University, Yixing, Jiangsu, China 2 Information and Data Center, The Affiliated Yixing Hospital of Jiangsu University, Yixing, ...