A hybrid ensemble model using Rough Set Theory and Dempster-Shafer Theory achieved an AUC of 0.715 for predicting major adverse cardiac events in patients with acute coronary syndrome, outperforming traditional single base models.
Does an ensemble machine learning approach using RST and DST improve MACE prediction in patients with acute coronary syndrome compared to single models?
An ensemble machine learning approach combining traditional risk scores and advanced algorithms via Rough Set and Dempster-Shafer theories improves the prediction of major adverse cardiac events in patients with acute coronary syndrome.
Absolute Event Rate: 0.715% vs 0.636%
BACKGROUND: Major adverse cardiac event (MACE) prediction plays a key role in providing efficient and effective treatment strategies for patients with acute coronary syndrome (ACS) during their hospitalizations. Existing prediction models have limitations to cope with imprecise and ambiguous clinical information such that clinicians cannot reach to reliable MACE prediction results for individuals. METHODS: -Logistic Regression, and Classification and Regression Tree, are employed to generate initial MACE prediction results, and then RST is applied to determine the weights of the four single models. After that, the acquired prediction results are assumed as basic beliefs for the problem propositions and in this way, an evidential prediction result is generated based on DST in an integrative manner. RESULTS: Having applied the proposed method on a clinical dataset consisting of 2930 ACS patient samples, our model achieves 0.715 AUC value with competitive standard deviation, which is the best prediction results comparing with the four single base models and two baseline ensemble models. CONCLUSIONS: Facing with the limitations in traditional ACS risk scoring models, machine learning models and the uncertainties of EHR data, we present an ensemble approach via RST and DST to alleviate this problem. The experimental results reveal that our proposed method achieves better performance for the problem of MACE prediction when compared with the single models.
Hu et al. (Mon,) conducted a other in Acute coronary syndrome (n=2,930). Hybrid RST-DST ensemble model vs. Single base models (GRACE, SVM, L1-LR, CART) and baseline ensemble models was evaluated on Area Under the Curve (AUC) for MACE prediction. A hybrid ensemble model using Rough Set Theory and Dempster-Shafer Theory achieved an AUC of 0.715 for predicting major adverse cardiac events in patients with acute coronary syndrome, outperforming traditional single base models.