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Integration of graph neural networks and long short-term memory models for advancing heart failure prediction

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Název: Integration of graph neural networks and long short-term memory models for advancing heart failure prediction
Autoři: Ibrahim Alrashdi, Ahmed I. Taloba
Zdroj: Alexandria Engineering Journal, Vol 127, Iss, Pp 143-163 (2025)
Informace o vydavateli: Elsevier BV, 2025.
Rok vydání: 2025
Témata: Heart failure prediction, Hybrid GNN-LSTM model, Long short-term memory, Predictive accuracy, Temporal dependencies, TA1-2040, Engineering (General). Civil engineering (General), Graph neural networks
Popis: Heart failure constitutes a chronic disease affecting millions of people worldwide, hence creating an important burden on healthcare infrastructures. Predictive models about the onset or worsening of HF can be instrumental in conducting proper and timely interventions to improve the outcomes of the care of patients with HF. This paper introduces a novel approach to predicting HF, integrating graph neural networks (GNNs) with long short-term memory (LSTM) networks for better prediction accuracy. This hybrid model, GNN-LSTM, applies the advantages of both networks: the complex interdependencies between clinical variables capture clinical relationships; LSTMs can better manage temporal dependencies. The model was tested on a large, highly representative dataset containing diversified clinical variables from HF patients, with 98.9 % predictive accuracy, which outperforms the single models as well as their respective performances by conventional methods like CNN, SMOTE, LSTM-RNN, CNN-LSTM, CNN-GRU, and traditional GNN approaches. Thus, the GNN-LSTM model, developed in Python, produces robust results across cases, irrespective of coronary heart disease co-presence comorbidity. Nonetheless, one of the limitations of the research is that generability is still in the future. This integrated approach has huge promises for improving HF prediction, with early interventions and personalized health strategies that would diminish the burden on patients and healthcare systems.
Druh dokumentu: Article
Jazyk: English
ISSN: 1110-0168
DOI: 10.1016/j.aej.2025.05.014
Přístupová URL adresa: https://doaj.org/article/5285dc4f59444012a544c4fcd150c5db
Rights: CC BY NC ND
Přístupové číslo: edsair.doi.dedup.....1f587ee5e76d718a27eb886a8f6701c0
Databáze: OpenAIRE
Popis
Abstrakt:Heart failure constitutes a chronic disease affecting millions of people worldwide, hence creating an important burden on healthcare infrastructures. Predictive models about the onset or worsening of HF can be instrumental in conducting proper and timely interventions to improve the outcomes of the care of patients with HF. This paper introduces a novel approach to predicting HF, integrating graph neural networks (GNNs) with long short-term memory (LSTM) networks for better prediction accuracy. This hybrid model, GNN-LSTM, applies the advantages of both networks: the complex interdependencies between clinical variables capture clinical relationships; LSTMs can better manage temporal dependencies. The model was tested on a large, highly representative dataset containing diversified clinical variables from HF patients, with 98.9 % predictive accuracy, which outperforms the single models as well as their respective performances by conventional methods like CNN, SMOTE, LSTM-RNN, CNN-LSTM, CNN-GRU, and traditional GNN approaches. Thus, the GNN-LSTM model, developed in Python, produces robust results across cases, irrespective of coronary heart disease co-presence comorbidity. Nonetheless, one of the limitations of the research is that generability is still in the future. This integrated approach has huge promises for improving HF prediction, with early interventions and personalized health strategies that would diminish the burden on patients and healthcare systems.
ISSN:11100168
DOI:10.1016/j.aej.2025.05.014