An XGBoost machine learning model utilizing non-parametric random forest imputation achieved an AUC of 0.8409 for predicting incident heart failure, identifying diabetes medication as a key risk factor.
Observational
Does a machine learning-based predictive model improve the prediction of incident heart failure in African Americans?
An XGBoost machine learning model with random forest imputation achieved an AUC of 0.8409 for predicting incident heart failure in African Americans, identifying diabetes medication variations as a key risk factor.
Effect estimate: AUC 0.8409
Background: Early detection of heart failure is the basis for better medical treatment and prognosis. Over the last decades, both prevalence and incidence rates of heart failure have increased worldwide, resulting in a significant global public health issue. However, an early diagnosis is not an easy task because symptoms of heart failure are usually non-specific. Therefore, this study aims to develop a risk prediction model for incident heart failure through a machine learning-based predictive model. Although African Americans have a higher risk of incident heart failure among all populations, few studies have developed a heart failure risk prediction model for African Americans. Methods: This research implemented the Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression, support vector machine, random forest, and Extreme Gradient Boosting (XGBoost) to establish the Jackson Heart Study’s predictive model. In the analysis of real data, missing data are problematic when building a predictive model. Here, we evaluate predictors’ inclusion with various missing rates and different missing imputation strategies to discover the optimal analytics. Results: According to hundreds of models that we examined, the best predictive model was the XGBoost that included variables with a missing rate of less than 30 percent, and we imputed missing values by non-parametric random forest imputation. The optimal XGBoost machine demonstrated an Area Under Curve (AUC) of 0.8409 to predict heart failure for the Jackson Heart Study. Conclusion: This research identifies variations of diabetes medication as the most crucial risk factor for heart failure compared to the complete cases approach that failed to discover this phenomenon.
Guo et al. (Thu,) conducted a observational in Heart failure. Machine learning predictive model (XGBoost) vs. Complete cases approach was evaluated on Incident heart failure (AUC 0.8409). An XGBoost machine learning model utilizing non-parametric random forest imputation achieved an AUC of 0.8409 for predicting incident heart failure, identifying diabetes medication as a key risk factor.