A new machine-learning method to prognosticate paraquat poisoned patients by combining coagulation, liver, and kidney indices

Lufeng Hu, Huaizhong Li, Zhennao Cai, Feiyan Lin, Guangliang Hong, Huiling Chen and Zhongqiu Lu

PLOS ONE, 2017, vol. 12, issue 10, 1-20

Abstract: The prognosis of paraquat (PQ) poisoning is highly correlated to plasma PQ concentration, which has been identified as the most important index in PQ poisoning. This study investigated the predictive value of coagulation, liver, and kidney indices in prognosticating PQ-poisoning patients, when aligned with plasma PQ concentrations. Coagulation, liver, and kidney indices were first analyzed by variance analysis, receiver operating characteristic curves, and Fisher discriminant analysis. Then, a new, intelligent, machine learning-based system was established to effectively provide prognostic analysis of PQ-poisoning patients based on a combination of the aforementioned indices. In the proposed system, an enhanced extreme learning machine wrapped with a grey wolf-optimization strategy was developed to predict the risk status from a pool of 103 patients (56 males and 47 females); of these, 52 subjects were deceased and 51 alive. The proposed method was rigorously evaluated against this real-life dataset, in terms of accuracy, Matthews correlation coefficients, sensitivity, and specificity. Additionally, the feature selection was investigated to identify correlating factors for risk status. The results demonstrated that there were significant differences in the coagulation, liver, and kidney indices between deceased and surviving subjects (p

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0186427

DOI: 10.1371/journal.pone.0186427

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