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Developing an Interpretable Machine Learning Model for Early Prediction of Cardiovascular Involvement in Systemic
Zixian Deng1, Huadong Liu1, Feng Chen2
1Department of Cardiology, Shenzhen People's Hospital (The First Affiliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University), Shenzhen, 518020, People's Republic of China.
Insights
This study developed an interpretable machine learning model to predict cardiac involvement in systemic lupus erythematosus (SLE). The Gradient Boosting Machine identified key predictors like arthritis and hypertension for early risk assessment in SLE patients.
Area of Science:
- Cardiology
- Rheumatology
- Artificial Intelligence
Background:
- Cardiovascular disease is a major cause of mortality in patients with systemic lupus erythematosus (SLE).
- Early detection of cardiac complications in SLE is crucial for improving patient prognosis and outcomes.
- This research focuses on identifying predictors and developing a predictive model for cardiac involvement in SLE.
Purpose of the Study:
- To identify key clinical and biological factors associated with cardiac involvement in SLE patients.
- To develop and validate an interpretable machine learning (ML) model for predicting the risk of cardiac complications in SLE.
- To enhance early identification and management of high-risk SLE individuals.
Main Methods:
- A retrospective analysis of 1,023 SLE patients hospitalized between 2000 and 2021.
- Utilized three feature selection techniques (Random Forest, LASSO, XGBoost) to identify predictive variables.
- Trained and tested seven ML algorithms, with Gradient Boosting Machine (GBM) selected for its performance and interpretability.
Main Results:
- Over a median follow-up of 3,737 days, 18.77% of patients developed cardiac involvement.
- Seven key predictors were identified: arthritis, hypertension, HDL-C, LDL-C, total cholesterol, CRP, and ESR.
- The GBM model achieved an AUC of 0.748 and an accuracy of 0.779.
Conclusions:
- This study presents the first interpretable ML model for predicting cardiac involvement risk in SLE.
- The developed GBM model demonstrates optimal performance and provides insights into decision-making processes for clinicians.
- The model facilitates early identification of SLE patients at high risk for cardiac complications.
Background:
Cardiovascular disease is a leading cause of death in systemic lupus erythematosus (SLE). Early prediction of cardiac involvement is critical for improving patient outcomes. This study aimed to identify key factors associated with cardiac involvement in SLE and to develop an interpretable machine learning (ML) model for risk prediction.
Methods:
We conducted a retrospective analysis of 1,023 SLE patients hospitalized in Shenzhen People's Hospital between January 2000 and December 2021, with a median age of 31 years at hospitalization (IQR: 25-39 years), 92.1% being female, and 18.77% developing cardiovascular involvement during a median follow-up of 3,737 days (IQR: 1,920-5,246). The most predictive features were selected through the intersection of three feature selection techniques: Random Forest, LASSO, and XGBoost. Models were trained on 70% of the dataset and tested on the remaining 30%. Among seven evaluated algorithms, the Gradient Boosting Machine (GBM) demonstrated the best performance on the test set. Model interpretability was assessed using the DALEX package, which generated feature importance plots and instance-level breakdown profiles to visualize decision-making logic.
Results:
Over a median follow-up of 3737 days, 192 (18.77%) patients developed cardiac involvement. Seven key predictors-arthritis, hypertension, HDL-C, LDL-C, total cholesterol, CRP, and ESR- were identified from 51 clinical and biological variables at admission. The Gradient Boosting Machine (GBM) model (AUC: 0.748, Accuracy: 0.779, Precision: 0.605, F1 score: 0.433, recall 0.338) performed the best of the seven models.
Conclusion:
This study is the first to develop an interpretable ML model to predict the risk of cardiac involvement in SLE. Notably, the GBM model showed optimal performance, and its interpretability allowed clinicians to visualize decision-making processes, facilitating early identification of high-risk patients.
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