Key result
An optimized XGBoost machine learning model integrating MICE imputation, SMOTETomek resampling, and spFSR feature selection achieved 93% accuracy and an AUC-ROC of 89% in predicting cardiovascular disease.
Why the study?
Cardiovascular disease is the leading cause of death worldwide, and primary prevention relies on early prediction of disease onset.
Does an optimized machine learning model integrating imputation, resampling, and feature selection improve the prediction of cardiovascular disease using laboratory data?
Observational (n=7,974)
Does an optimized machine learning model integrating imputation, resampling, and feature selection improve the prediction of cardiovascular disease using laboratory data?
An optimized XGBoost machine learning model integrating MICE, SMOTETomek, and spFSR significantly improves the prediction of cardiovascular disease using laboratory data.
Answers are grounded in this paper and cite their sources.
May improve lab-based CVD prediction; leaves open need for prospective validation before clinical use.
Cardiovascular disease (CVD) is the leading cause of death worldwide. Primary prevention is by early prediction of the disease onset. Using laboratory data from the National Health and Nutrition Examination Survey (NHANES) in 2017-2020 timeframe (N= 7.974), we tested the ability of machine learning (ML) algorithms to classify individuals at risk. The ML models were evaluated based on their classification performances after comparing four imputation, three imbalance resampling, and three feature selection techniques.Due to its popularity, we utilized decision tree (DT) as the baseline. Integration of multiple imputation by chained equation (MICE) and synthetic minority oversampling with Tomek link down-sampling (SMOTETomek) into the model improved the area under the curve-receiver operating characteristics (AUC-ROC) from 57% to 83%. Applying simultaneous perturbation feature selection and ranking (spFSR) reduced the feature predictors from 144 to 30 features and the computational time by 22%. The best techniques were applied to six ML models, resulting in Xtreme gradient boosting (XGBoost) achieving the highest accuracy of 93% and AUC-ROC of 89%.The accuracy of our ML model in predicting CVD outperforms those from previous studies. We also highlight the important causes of CVD, which might be investigated further for potential effects on electronic health records.
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Alfebi et al. (2023) conducted an observational in Cardiovascular disease (n=7,974). XGBoost machine learning model with MICE, SMOTETomek, and spFSR vs. Baseline Decision Tree model with simple mean imputation was evaluated on Area under the receiver operating characteristics curve (AUC-ROC) for cardiovascular disease prediction. An optimized XGBoost machine learning model integrating MICE imputation, SMOTETomek resampling, and spFSR feature selection achieved 93% accuracy and an AUC-ROC of 89% in predicting cardiovascular disease.
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