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Machine Learning-Based Cardiovascular Disease Prediction Model: A Cohort Study on the Korean National Health
Joung Ouk Ryan Kim1, Yong-Suk Jeong2, Jin Ho Kim1
1Department of AI and Big Data, Swiss School of Management, 6500 Bellinzona, Switzerland.
Insights
Machine learning models accurately predict cardiovascular diseases (CVD) using health screening data. These models, particularly extreme gradient boosting, show improved prediction performance over previous methods.
Area of Science:
- Cardiovascular disease research
- Machine learning applications in healthcare
- Public health data analysis
Background:
- Cardiovascular diseases (CVD) pose a significant global health challenge.
- Accurate prediction models are crucial for early intervention and management.
- Leveraging large-scale health screening datasets offers potential for improved CVD risk assessment.
Purpose of the Study:
- To develop and validate a cardiovascular disease (CVD) prediction model.
- To evaluate the performance of various machine learning (ML) algorithms for CVD prediction.
- To identify key risk factors for CVD within the study population.
Main Methods:
- Utilized the National Health Insurance Service-Health Screening dataset.
- Included 4699 patients aged over 45 diagnosed with CVD (ICD-10: I20-I25) as the CVD group.
- Matched 4699 non-CVD subjects by age and gender; applied and compared multiple ML algorithms for prediction.
Main Results:
- Extreme gradient boosting, gradient boosting, and random forest algorithms achieved the highest prediction accuracy (AUROC: 0.812, 0.812, 0.811).
- ML algorithms demonstrated superior CVD prediction performance compared to existing models.
- Preexisting CVD history, total cholesterol, LDL cholesterol, waist-height ratio, and BMI were key predictors.
Conclusions:
- The proposed ML-based CVD prediction model using health screening data is effective and validated.
- The model demonstrates superior performance compared to previous CVD prediction approaches.
- This approach offers a practical tool for enhancing cardiovascular disease risk assessment.
Background:
This study proposes a cardiovascular diseases (CVD) prediction model using machine learning (ML) algorithms based on the National Health Insurance Service-Health Screening datasets.
Methods:
We extracted 4699 patients aged over 45 as the CVD group, diagnosed according to the international classification of diseases system (I20-I25). In addition, 4699 random subjects without CVD diagnosis were enrolled as a non-CVD group. Both groups were matched by age and gender. Various ML algorithms were applied to perform CVD prediction; then, the performances of all the prediction models were compared.
Results:
The extreme gradient boosting, gradient boosting, and random forest algorithms exhibited the best average prediction accuracy (area under receiver operating characteristic curve (AUROC): 0.812, 0.812, and 0.811, respectively) among all algorithms validated in this study. Based on AUROC, the ML algorithms improved the CVD prediction performance, compared to previously proposed prediction models. Preexisting CVD history was the most important factor contributing to the accuracy of the prediction model, followed by total cholesterol, low-density lipoprotein cholesterol, waist-height ratio, and body mass index.
Conclusions:
Our results indicate that the proposed health screening dataset-based CVD prediction model using ML algorithms is readily applicable, produces validated results and outperforms the previous CVD prediction models.
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