Prediction of ACS mortality risk based on feature interaction and re-weighting

Wen Fang; Qiao Pan; Dehua Chen; Mei Wang

doi:10.1117/12.2675114

25 May 2023 Prediction of ACS mortality risk based on feature interaction and re-weighting

Wen Fang, Qiao Pan, Dehua Chen, Mei Wang

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 126364T (2023) https://doi.org/10.1117/12.2675114
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

Acute coronary syndrome (ACS) is a serious coronary heart disease with a high morbidity and mortality rate. The prognosis and the occurrence of adverse events vary according to the degree of vascular disease in patients with ACS. Early identification of high-risk groups therefore plays an important role in clinical management. Risk scoring tools are widely used in clinical practice, focus on converting specific prognostic factors into risk indices and then combining them with logistic regression to make predictions. These methods focus well on statistical correlations in the data, but ignore the instability associated with differences in the distribution of data in real data sets and also lack a focus on treatment information in the context of the disease. To address these issues, this paper proposes a model for predicting the risk of death in ACS based on feature interaction and re-weighting (i.e. FIFR Stable-Net). The model is able to take into account differences in the distribution of data features, and enhances feature learning by re-weighting risk features to reduce the impact of differences in the distribution of patient feature, thereby enhancing model stability. In addition, the model incorporates treatment features such as patient medication and applies an attention-based mechanism of feature interaction to obtain a more informative feature representation for the ACS mortality risk prediction task. To illustrate the effectiveness of the FIFR Stable-Net, we evaluated our method on a real dataset of ACS patients and compared it experimentally with the baseline models. The results showed that our model outperformed other models in terms of AUC and other assessment metrics, and AUC can reach up to 91.57%.

Citation Download Citation

Wen Fang, Qiao Pan, Dehua Chen, and Mei Wang "Prediction of ACS mortality risk based on feature interaction and re-weighting", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 126364T (25 May 2023); https://doi.org/10.1117/12.2675114

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KEYWORDS

Machine learning

Data modeling

Diseases and disorders

Feature extraction

Transformers

Performance modeling

Cardiovascular disorders

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