J Biomed Inform - Decision-making model for early diagnosis of congestive heart failure using rough set and decision tree approaches.

Tópicos

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Resumo

The accurate diagnosis of heart failure in emergency room patients is quite important, but can also be quite difficult due to our insufficient understanding of the characteristics of heart failure. The purpose of this study is to design a decision-making model that provides critical factors and knowledge associated with congestive heart failure (CHF) using an approach that makes use of rough sets (RSs) and decision trees. Among 72 laboratory findings, it was determined that two subsets (RBC, EOS, Protein, O2SAT, Pro BNP) in an RS-based model, and one subset (Gender, MCHC, Direct bilirubin, and Pro BNP) in a logistic regression (LR)-based model were indispensable factors for differentiating CHF patients from those with dyspnea, and the risk factor Pro BNP was particularly so. To demonstrate the usefulness of the proposed model, we compared the discriminatory power of decision-making models that utilize RS- and LR-based decision models by conducting 10-fold cross-validation. The experimental results showed that the RS-based decision-making model (accuracy: 97.5%, sensitivity: 97.2%, specificity: 97.7%, positive predictive value: 97.2%, negative predictive value: 97.7%, and area under ROC curve: 97.5%) consistently outperformed the LR-based decision-making model (accuracy: 88.7%, sensitivity: 90.1%, specificity: 87.5%, positive predictive value: 85.3%, negative predictive value: 91.7%, and area under ROC curve: 88.8%). In addition, a pairwise comparison of the ROC curves of the two models showed a statistically significant difference (p<0.01; 95% CI: 2.63-14.6).

Resumo Limpo

accur diagnosi heart failur emerg room patient quit import can also quit difficult due insuffici understand characterist heart failur purpos studi design decisionmak model provid critic factor knowledg associ congest heart failur chf use approach make use rough set rss decis tree among laboratori find determin two subset rbc eo protein osat pro bnp rsbase model one subset gender mchc direct bilirubin pro bnp logist regress lrbase model indispens factor differenti chf patient dyspnea risk factor pro bnp particular demonstr use propos model compar discriminatori power decisionmak model util rs lrbase decis model conduct fold crossvalid experiment result show rsbase decisionmak model accuraci sensit specif posit predict valu negat predict valu area roc curv consist outperform lrbase decisionmak model accuraci sensit specif posit predict valu negat predict valu area roc curv addit pairwis comparison roc curv two model show statist signific differ p ci

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