title: Using Blood Indexes to Predict Overweight Statuses: An Extreme Learning Machine-Based Approach.
abstract: The number of the overweight people continues to rise across the world. Studies have shown that being overweight can increase health risks, such as high blood pressure, diabetes mellitus, coronary heart disease, and certain forms of cancer. Therefore, identifying the overweight status in people is critical to prevent and decrease health risks. This study explores a new technique that uses blood and biochemical measurements to recognize the overweight condition. A new machine learning technique, an extreme learning machine, was developed to accurately detect the overweight status from a pool of 225 overweight and 251 healthy subjects. The group included 179 males and 297 females. The detection method was rigorously evaluated against the real-life dataset for accuracy, sensitivity, specificity, and AUC (area under the receiver operating characteristic (ROC) curve) criterion. Additionally, the feature selection was investigated to identify correlating factors for the overweight status. The results demonstrate that there are significant differences in blood and biochemical indexes between healthy and overweight people (p-value < 0.01). According to the feature selection, the most important correlated indexes are creatinine, hemoglobin, hematokrit, uric Acid, red blood cells, high density lipoprotein, alanine transaminase, triglyceride, and γ-glutamyl transpeptidase. These are consistent with the results of Spearman test analysis. The proposed method holds promise as a new, accurate method for identifying the overweight status in subjects.
authors.affiliation: College of Physics and Electronic Information Engineering, Wenzhou University, Wenzhou, China.|Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China.
authors.forename: Huiling
authors.initials: H
authors.lastname: Chen
authors.affiliation: College of Computer Science and Technology, Jilin University, Changchun, China.|Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China.
authors.forename: Bo
authors.initials: B
authors.lastname: Yang
authors.affiliation: College of Computer Science and Technology, Jilin University, Changchun, China.|Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China.
authors.forename: Dayou
authors.initials: D
authors.lastname: Liu
authors.affiliation: College of Physics and Electronic Information Engineering, Wenzhou University, Wenzhou, China.
authors.forename: Wenbin
authors.initials: W
authors.lastname: Liu
authors.affiliation: College of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, China.
authors.forename: Yanlong
authors.initials: Y
authors.lastname: Liu
authors.affiliation: Pharmaceutical Department, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
authors.forename: Xiuhua
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Pharmaceutical Department, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
authors.forename: Lufeng
authors.initials: L
authors.lastname: Hu
mesh_terms: D019540:Area Under Curve; D016022:Case-Control Studies; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008297:Male; D008954:Models, Biological; D016571:Neural Networks, Computer; D050177:Overweight / Q000097:blood*; D015203:Reproducibility of Results; D060388:Support Vector Machine