Predictive model and risk analysis for coronary heart disease in people living with HIV using machine learning
Zengjing Liu1, Zhihao Meng2, Di Wei2
1Information and Management College of Guangxi Medical University, Nanning, Guangxi, 530021, China.
Insights
Machine learning models can predict coronary heart disease (CHD) risk in people living with human immunodeficiency virus (PLHIV) using electronic medical records. The LightGBM model showed the best performance, identifying key risk factors for improved patient care.
Area of Science:
- Cardiology
- Infectious Diseases
- Data Science
Background:
- People living with human immunodeficiency virus (PLHIV) have an increased risk of coronary heart disease (CHD).
- Accurate CHD risk prediction is crucial for managing cardiovascular health in PLHIV.
- Electronic medical records (EMRs) offer a rich data source for developing predictive models.
Purpose of the Study:
- To construct and validate a CHD risk-prediction model for PLHIV using machine learning (ML) and EMR data.
- To identify key predictors of CHD risk in the PLHIV population.
- To leverage ML for enhanced clinical decision-making in PLHIV care.
Main Methods:
- Utilized 61 medical characteristics from EMRs for analysis.
- Developed and compared seven ML algorithms: LightGBM, SVM, XGBoost, AdaBoost, decision tree, MLP, and logistic regression.
- Assessed model performance using the area under the receiver operating characteristic curve (AUC) and employed SHAP for interpretation.
Main Results:
- The LightGBM model achieved the highest AUC (0.849), indicating superior predictive performance.
- Key predictors identified by the LightGBM model included age, heart failure, hypertension, glucose, serum creatinine, indirect bilirubin, serum uric acid, and amylase.
- These factors effectively differentiated PLHIV at high or low risk for CHD.
Conclusions:
- A robust CHD risk prediction model for PLHIV was successfully developed using ML and EMR data.
- The LightGBM model demonstrates high reliability and potential for clinical application in managing PLHIV.
- This approach can facilitate the development of targeted clinical management strategies for PLHIV in the EMR era.
Objective:
This study aimed to construct a coronary heart disease (CHD) risk-prediction model in people living with human immunodeficiency virus (PLHIV) with the help of machine learning (ML) per electronic medical records (EMRs).
Methods:
Sixty-one medical characteristics (including demography information, laboratory measurements, and complicating disease) readily available from EMRs were retained for clinical analysis. These characteristics further aided the development of prediction models by using seven ML algorithms [light gradient-boosting machine (LightGBM), support vector machine (SVM), eXtreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), decision tree, multilayer perceptron (MLP), and logistic regression]. The performance of this model was assessed using the area under the receiver operating characteristic curve (AUC). Shapley additive explanation (SHAP) was further applied to interpret the findings of the best-performing model.
Results:
The LightGBM model exhibited the highest AUC (0.849; 95% CI, 0.814-0.883). Additionally, the SHAP plot per the LightGBM depicted that age, heart failure, hypertension, glucose, serum creatinine, indirect bilirubin, serum uric acid, and amylase can help identify PLHIV who were at a high or low risk of developing CHD.
Conclusion:
This study developed a CHD risk prediction model for PLHIV utilizing ML techniques and EMR data. The LightGBM model exhibited improved comprehensive performance and thus had higher reliability in assessing the risk predictors of CHD. Hence, it can potentially facilitate the development of clinical management techniques for PLHIV care in the era of EMRs.
