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Updated: Aug 11, 2025
Pulse Wave Velocity Testing in the Baltimore Longitudinal Study of Aging
Published on: February 7, 2014
Logistic Regression Model Based on Ultrafast Pulse Wave Velocity and Different Feature Selection Methods to Predict
Xue Bai1, Wenjun Liu1, Hui Huang2
1School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China.
Insights
Early hypertension diagnosis is crucial for cardiovascular disease prevention. Logistic regression with Random Forest feature selection achieved high accuracy (0.910) and AUC (0.924) in predicting hypertension, identifying key imaging indicators.
Area of Science:
- Cardiology
- Medical Informatics
- Biostatistics
Background:
- Hypertension is a primary driver of increasing cardiovascular disease incidence.
- Accurate and early hypertension diagnosis is essential for disease prevention.
- Improved diagnostic accuracy necessitates advanced predictive models.
Purpose of the Study:
- To enhance the accuracy of hypertension diagnosis using logistic regression.
- To evaluate the effectiveness of different feature selection methods for logistic regression models.
- To identify key imaging indicators for hypertension prediction.
Main Methods:
- Collected 397 samples (178 hypertension, 219 control) from Nanjing, China (2016-2017).
- Utilized clinical, laboratory, and imaging data, focusing on imaging attribute differences.
- Applied three feature selection methods: statistical analysis, Random Forest (RF), and Extreme Gradient Boosting (XGBoost) to logistic regression.
Main Results:
- Logistic regression with RF feature selection yielded the highest accuracy (0.910) and AUC (0.924).
- LR with XGBoost achieved 0.897 accuracy and 0.915 AUC.
- LR with statistical analysis showed 0.872 accuracy and 0.926 AUC.
Conclusions:
- Logistic regression combined with Random Forest feature selection demonstrates strong potential for accurate hypertension prediction.
- Carotid intima-media thickness (cIMT) and end-systolic pulse wave velocity (ESPWV) are significant imaging predictors of hypertension.
Background:
Hypertension is the main reason why the incidence of cardiovascular disease has increased year-by-year and early diagnosis of hypertension is necessary to reducing the incidence of cardiovascular disease. This also puts forward higher requirements for the accuracy of diagnosis. We tried a variety of feature selection methods to improve the accuracy of logistic regression (LR).
Methods:
We collected 397 samples from Nanjing, Jiangsu, China between Jan 2016 and Dec 2017, including 178 hypertension samples and 219 control samples. It includes not only clinical and laboratory data, but also imaging data. We focused on the difference of imaging attributes between the control group and the hypertension group, and analyzed the correlation coefficients of all attributes. In order to establish the optimal LR model, this study tried three different feature selection methods, including statistical analysis, random forest (RF) and extreme gradient boosting (XGBoost). The area under the ROC curve (AUC) and accuracy were used as the main criterion for model evaluation.
Results:
In the prediction of hypertension, the performance of LR with RF as the feature selection method (accuracy: 0.910; AUC: 0.924) was better than the performance of LR with XGBoost as the feature selection method (accuracy: 0.897; AUC: 0.915) and the performance of LR with statistical analysis as the feature selection method (accuracy: 0.872; AUC: 0.926).
Conclusion:
LR with RF as the feature selection method may provide accurate results in predicting hypertension. Carotid intima-media thickness (cIMT) and pulse wave velocity at the end of systole (ESPWV) are two key imaging indicators in the prediction of hypertension.
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