title: An efficient machine learning approach for diagnosis of paraquat-poisoned patients.
abstract: Numerous people die of paraquat (PQ) poisoning because they were not diagnosed and treated promptly at an early stage. Till now, determination of PQ levels in blood or urine is still the only way to confirm the PQ poisoning. In order to develop a new diagnostic method, the potential of machine learning technique was explored in this study. A newly developed classification technique, extreme learning machine (ELM), was taken to discriminate the PQ-poisoned patients from the healthy controls. 15 PQ-poisoned patients recruited from The First Affiliated Hospital of Wenzhou Medical University who had a history of direct contact with PQ and 16 healthy volunteers were involved in the study. The ELM method is examined based on the metabolites of blood samples determined by gas chromatography coupled with mass spectrometry in terms of classification accuracy, sensitivity, specificity and AUC (area under the receiver operating characteristic (ROC) curve) criterion, respectively. Additionally, the feature selection was also investigated to further boost the performance of ELM and the most influential feature was detected. The experimental results demonstrate that the proposed approach can be regarded as a success with the excellent classification accuracy, AUC, sensitivity and specificity of 91.64%, 0.9156%, 91.33% and 91.78%, respectively. Promisingly, the proposed method might serve as a new candidate of powerful tools for diagnosis of PQ-poisoned patients with excellent performance.
authors.affiliation: The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.
authors.forename: Lufeng
authors.initials: L
authors.lastname: Hu
authors.affiliation: Department of Emergency, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.
authors.forename: Guangliang
authors.initials: G
authors.lastname: Hong
authors.affiliation: Function Experiment Teaching Center, Wenzhou Medical University, 325035 Wenzhou, China.
authors.forename: Jianshe
authors.initials: J
authors.lastname: Ma
authors.affiliation: Function Experiment Teaching Center, Wenzhou Medical University, 325035 Wenzhou, China.
authors.forename: Xianqin
authors.initials: X
authors.lastname: Wang
authors.affiliation: College of Physics and Electronic Information Engineering, Wenzhou University, 325035 Wenzhou, China. Electronic address: chenhuiling.jlu@gmail.com.
authors.forename: Huiling
authors.initials: H
authors.lastname: Chen
mesh_terms: D000293:Adolescent; D000328:Adult; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D010269:Paraquat / Q000506:poisoning*; D011041:Poisoning / Q000175:diagnosis*; D012372:ROC Curve; D055815:Young Adult
chemical_list: D010269:Paraquat
keywords: Extreme learning machine; Medical diagnosis; Paraquat; Poison