Chaos enhanced grey wolf optimization wrapped ELM for diagnosis of paraquat-poisoned patients |
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| Institution: | 1. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China;2. School of Computer Science, Shaanxi Normal University, Xi’an, China;3. Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China;4. School of Science, Beijing University of Posts and Telecommunications, Beijing, China;1. Department of Pulmonary and Critical Care Medicine,The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325035, China;2. Department of Neurology, Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine, Wenzhou 325027, China;3. Jawaharlal Nehru Hospital, Rose Belle, Grand-Port District 00230, Mauritius;4. Department of Computing, Lishui University, Lishui 323000, Zhejiang, China;5. School of Digital Media, Shenzhen Institute of Information Technology, Shenzhen 518172, China;6. College of Physics and Electronic Information Engineering, Wenzhou University, Wenzhou 325035, China |
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| Abstract: | 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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| Keywords: | Diagnosis Paraquat Chaos Grey wolf optimization Extreme learning machine |
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