Risk prediction of heart failure in patients with ischemic heart disease using network analytics and stacking ensemble learning

Abstract

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 F₁ 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 F₁ 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.

Keywords: Administrative data; Comorbidity network; Ensemble learning; Heart failure; Ischemic heart disease; Network feature.

Fig. 1

The overview of the framework for predicting HF risk in IHD patients. PDNs: Personal Disease Networks. BDNs: Baseline Disease Networks. DSN: Disease-Specific Network. DXLR: the two-stage ensemble machine learning model our study proposed

Fig. 2

The extraction process for two patient cohorts

Fig. 3

The process of constructing networks. Nodes represent diseases and directed edges represent the sequential relationship between diseases. HDRs: Hospital Discharge Records. C_IHD&HF: Cohort of patients with IHD and HF. C_IHD: Cohort of patients with IHD. PDNs: Personal Disease Networks. (adapted from [37]). BDN: Baseline Disease Network. DSN: Disease-Specific Network

Fig. 4

Visualization of the Disease-Specific Network. Nodes represent diseases and node sizes represent disease prevalence. Directed edges represent the sequence of occurrence between diseases and the frequency of two diseases that occurred during the same or consecutive admissions. Edges weighted less than 0.5 are hidden for simplicity

Fig. 5

The overall framework of DXLR.

Fig. 6

SHAP summary plot of the DXLR model. Average absolute impact of features on the final model output magnitude ordered by decreasing feature importance. Rank: the rank-based score. Edge: the edge score. Node: the node score