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A Cardiovascular Disease Prediction Model Based on Routine Physical Examination Indicators Using Machine Learning
Xin Qian1, Yu Li1, Xianghui Zhang1
1Department of Public Health, Shihezi University School of Medicine, Shihezi, China.
Insights
Machine learning models can predict cardiovascular diseases (CVD) in Xinjiang
Area of Science:
- Public Health
- Medical Informatics
- Epidemiology
Background:
- Cardiovascular diseases (CVD) are a leading global cause of premature death.
- Early detection of high-risk populations is crucial for CVD prevention.
- This study focuses on the Xinjiang rural population, a region with unique health considerations.
Purpose of the Study:
- To develop a machine learning (ML) model for predicting CVD risk.
- To identify key indicators for CVD prediction using routine physical examination data.
- To establish a model suitable for the Xinjiang rural population.
Main Methods:
- Utilized data from two-stage surveys (2010-2017 and 2016-2021) with 12,692 participants.
- Employed feature selection techniques including Lasso regression, FLR, and RF.
- Compared prediction models: L1-LR, RF, SVM, and AdaBoost for CVD risk assessment.
Main Results:
- Identified key predictors: age, systolic blood pressure, lipid profile indices, triglyceride-glucose index, BMI, and BAI.
- The L1-LR model demonstrated superior prediction performance in discrimination and calibration.
- The cumulative incidence of CVD was 9.27% after a 4.94-year follow-up.
Conclusions:
- The L1-LR based prediction model shows the best performance for the Xinjiang rural population.
- Routine physical examination indicators can effectively predict CVD risk in this demographic.
- This model aids in early detection and prevention strategies for CVD in the region.
Background:
Cardiovascular diseases (CVD) are currently the leading cause of premature death worldwide. Model-based early detection of high-risk populations for CVD is the key to CVD prevention. Thus, this research aimed to use machine learning (ML) algorithms to establish a CVD prediction model based on routine physical examination indicators suitable for the Xinjiang rural population.
Method:
The research cohort data collection was divided into two stages. The first stage involved a baseline survey from 2010 to 2012, with follow-up ending in December 2017. The second-phase baseline survey was conducted from September to December 2016, and follow-up ended in August 2021. A total of 12,692 participants (10,407 Uyghur and 2,285 Kazak) were included in the study. Screening predictors and establishing variable subsets were based on least absolute shrinkage and selection operator (Lasso) regression, logistic regression forward partial likelihood estimation (FLR), random forest (RF) feature importance, and RF variable importance. The selected subset of variables was compared with L1 regularized logistic regression (L1-LR), RF, support vector machine (SVM), and AdaBoost algorithm to establish a CVD prediction model suitable for this population. The incidence of CVD in this population was then analyzed.
Result:
After 4.94 years of follow-up, a total of 1,176 people were diagnosed with CVD (cumulative incidence: 9.27%). In the comparison of discrimination and calibration, the prediction performance of the subset of variables selected based on FLR was better than that of other models. Combining the results of discrimination, calibration, and clinical validity, the prediction model based on L1-LR had the best prediction performance. Age, systolic blood pressure, low-density lipoprotein-L/high-density lipoproteins-C, triglyceride blood glucose index, body mass index, and body adiposity index were all important predictors of the onset of CVD in the Xinjiang rural population.
Conclusion:
In the Xinjiang rural population, the prediction model based on L1-LR had the best prediction performance.
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