Chaos enhanced grey wolf optimization wrapped ELM for diagnosis of paraquat-poisoned patients

By Xuehua Zhao, Xiang Zhang, Zhennao Cai, Xin Tian, Xianqin Wang, Ying Huang, Huiling Chen, Lufeng Hu. Comput. Biol. Chem., 2019

302 citations recorded by the source corpus.

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.

Subfields: Complementary and alternative medicine, Surgery

View Paper: https://www.semanticscholar.org/paper/f25aeb5e03aaaa4f0a6cdf0e80fa69bcb56fe32f

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Citations in the corpus

A Novel Deep-Learning Model for Automatic Detection and Classification of Breast Cancer Using the Transfer-Learning Technique (2021, methodology): “In future work, the proposed method can be further used to diagnosis or prognosis of paraquat-poisoned patients [104]–[109], identiﬁcation of poisoning status [110]–[112], diagnosis of tuberculous pleural effusion [113], differentiation of malignant and benign thyroid nodules [114], early diagnosis…”