Abstract
BackgroundMajor 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.MethodsTo remedy it, this study proposes a hybrid method using Rough Set Theory (RST) and Dempster-Shafer Theory (DST) of evidence. In details, four state-of-the-art models, including one traditional ACS risk scoring model, i.e., GRACE, and three machine learning based models, i.e., Support Vector Machine, L1-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.ResultsHaving 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.ConclusionsFacing 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.
Highlights
MethodsThis study proposes a hybrid method using Rough Set Theory (RST) and Dempster-Shafer Theory (DST) of evidence
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
We propose a hybrid method using Rough Set Theory [19] (RST) and Dempster-Shafer Theory of evidence for MACE prediction
Summary
We propose an ensemble approach to integrate traditional risk scoring models and advanced machine learning based models together to alleviate the limitations we mentioned above. 1⁄41; ð6Þ where A∗i, j is the adjusted output of ith model for the jth patient with i∈{SVM, L1-LR, CART, GRACE}, Thresholdi is the ith model’s optimal threshold utilized in the dichotomization procedure for weights calculation using RST. Experiments and results Based on our previous work, we have obtained the original outputs of the four single models, e.g., SVM, L1-LR, CART and GRACE, for a total of 2930 ACS patient samples collected from the Cardiology Department of the Chinese PLA General Hospital.
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