Machine Learning Models for Cardiovascular Disease Events Prediction
Insights
Machine learning models predict cardiovascular disease (CVD) mortality. Logistic Regression achieved the highest accuracy, identifying high-risk patients for early intervention and improved outcomes.
Area of Science:
- Cardiovascular medicine
- Biostatistics
- Machine learning in healthcare
Background:
- Cardiovascular diseases (CVDs) represent a significant global health burden, contributing to high mortality rates worldwide.
- Early diagnosis and prevention strategies are crucial for managing CVDs, necessitating the identification of reliable risk biomarkers.
- Machine learning (ML) offers powerful tools for analyzing complex clinical and biochemical data to predict disease outcomes.
Purpose of the Study:
- To evaluate the efficacy of various machine learning models in predicting 10-year cardiovascular disease (CVD) mortality.
- To identify the most accurate ML algorithm for CVD risk assessment using clinical and biochemical data.
- To provide a foundation for risk stratification tools like the TIMELY study.
Main Methods:
- Utilized the Ludwigshafen Risk and Cardiovascular Health (LURIC) study cohort, including 2943 patients (484 deceased due to CVD).
- Applied multiple machine learning models: Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), Extreme Grading Boosting (XGB), and Adaptive Boosting (AdaBoost).
- Assessed model performance using metrics such as Accuracy (ACC), Precision, Recall, F1-Score, Specificity (SPE), and Area Under the ROC Curve (AUC).
Main Results:
- Logistic Regression (LR) demonstrated the highest predictive performance with an accuracy of 72.20%.
- Comparative analysis evaluated the performance of six distinct machine learning algorithms for CVD mortality prediction.
- The study identified specific ML models suitable for clinical application in risk assessment.
Conclusions:
- Logistic Regression is a reliable algorithm for predicting 10-year CVD mortality based on clinical and biochemical data.
- The findings support the integration of ML-driven risk scores for proactive CVD management.
- These results will inform the TIMELY study for estimating CVD risk and mortality in patient populations.
Abstract:
Cardiovascular diseases (CVDs) are among the most serious disorders leading to high mortality rates worldwide. CVDs can be diagnosed and prevented early by identifying risk biomarkers using statistical and machine learning (ML) models, In this work, we utilize clinical CVD risk factors and biochemical data using machine learning models such as Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), Extreme Grading Boosting (XGB) and Adaptive Boosting (AdaBoost) to predict death caused by CVD within ten years of follow-up. We used the cohort of the Ludwigshafen Risk and Cardiovascular Health (LURIC) study and 2943 patients were included in the analysis (484 annotated as dead due to CVD). We calculated the Accuracy (ACC), Precision, Recall, F1-Score, Specificity (SPE) and area under the receiver operating characteristic curve (AUC) of each model. The findings of the comparative analysis show that Logistic Regression has been proven to be the most reliable algorithm having accuracy 72.20 %. These results will be used in the TIMELY study to estimate the risk score and mortality of CVD in patients with 10-year risk.
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