<p class="first" id="d5570710e164">Paraquat (PQ) poisoning seriously harms the health
of humanity. An effective diagnostic
method for paraquat poisoned patients is a crucial concern. Nevertheless, it's difficult
to identify the patients with low intake of PQ or delayed treatment. Here, a new efficient
diagnostic approach to integrate machine learning and gas chromatography-mass spectrometry
(GC-MS), named GEE, is proposed to identify the PQ poisoned patients. First, GC-MS
provides the original data that efficiently identified the paraquat-poisoned patients.
According to the high dimensionality of the original data, in the second stage, the
chaos enhanced grey wolf optimization (EGWO) is adopted to search the optimal feature
sets to improve the accuracy of identification. Finally, the extreme learning machine
(ELM) is used to identify the PQ poisoned patients. To efficiently evaluate the proposed
method, four measures were used in our experiments and comparisons were made with
six other methods. The PQ-poisoned patients and robust volunteers can be well identified
by GEE and the values of AUC, accuracy, sensitivity and specificity were 95.14%, 93.89%,
94.44% and 95.83%, respectively. Our experimental results demonstrated that GEE had
better performance and might serve as a novel candidate diagnosis of PQ-poisoned patients.
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