Key result
The DXLR stacking ensemble model, utilizing novel network features and demographic data, predicted heart failure risk in patients with ischemic heart disease with an AUC of 0.934, significantly outperforming traditional machine learning models.
Why the study?
Heart failure is a major adverse complication following ischemic heart disease, making early risk prediction beneficial for timely intervention and reducing disease burden.
Does a stacking-based ensemble machine learning model using network analytics improve the prediction of heart failure risk in patients with ischemic heart disease compared to traditional machine learning models?
Cohort (n=37,514)
Yes
Does a stacking-based ensemble machine learning model using network analytics improve the prediction of heart failure risk in patients with ischemic heart disease compared to traditional machine learning models?
Absolute Event Rate: 0.934% vs 0.928%
p-value: p=<0.0001
A stacking-based ensemble machine learning model incorporating network analytics significantly improves the prediction of heart failure risk in patients with ischemic heart disease using administrative data.
May support HF risk prediction models in IHD; hypothesis-generating and requires prospective validation before practice change.
Background Heart failure (HF) is a major complication following ischemic heart disease (IHD) and it adversely affects the outcome. Early prediction of HF risk in patients with IHD is beneficial for timely intervention and for reducing disease burden. Methods Two cohorts, cases for patients first diagnosed with IHD and then with HF (N = 11,862) and control IHD patients without HF (N = 25,652), were established from the hospital discharge records in Sichuan, China during 2015-2019. Directed personal disease network (PDN) was constructed for each patient, and then these PDNs were merged to generate the baseline disease network (BDN) for the two cohorts, respectively, which identifies the health trajectories of patients and the complex progression patterns. The differences between the BDNs of the two cohort was represented as disease-specific network (DSN). Three novel network features were exacted from PDN and DSN to represent the similarity of disease patterns and specificity trends from IHD to HF. A stacking-based ensemble model DXLR was proposed to predict HF risk in IHD patients using the novel network features and basic demographic features (i.e., age and sex). The Shapley Addictive exPlanations method was applied to analyze the feature importance of the DXLR model. Results Compared with the six traditional machine learning models, our DXLR model exhibited the highest AUC (0.934 ± 0.004), accuracy (0.857 ± 0.007), precision (0.723 ± 0.014), recall (0.892 ± 0.012) and F 1 score (0.798 ± 0.010). The feature importance showed that the novel network features ranked as the top three features, playing a notable role in predicting HF risk of IHD patient. The feature comparison experiment also indicated that our novel network features were superior to those proposed by the state-of-the-art study in improving the performance of the prediction model, with an increase in AUC by 19.9%, in accuracy by 18.7%, in precision by 30.7%, in recall by 37.4%, and in F 1 score by 33.7%. Conclusions Our proposed approach that combines network analytics and ensemble learning effectively predicts HF risk in patients with IHD. This highlights the potential value of network-based machine learning in disease risk prediction field using administrative data.
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Zhou et al. (2023) conducted a cohort in Ischemic heart disease at risk of heart failure (n=37,514). DXLR stacking-based ensemble model vs. Traditional machine learning models (e.g., XGBoost) was evaluated on Area under the receiver operating characteristic curve (AUC) for predicting heart failure (p=<0.0001). The DXLR stacking ensemble model, utilizing novel network features and demographic data, predicted heart failure risk in patients with ischemic heart disease with an AUC of 0.934, significantly outperforming traditional machine learning models.
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