Using Blood Indexes to Predict Overweight Statuses: An Extreme Learning Machine-Based Approach

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 c...

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Main Authors:	Chen, Huiling, Yang, Bo, Liu, Dayou, Liu, Wenbin, Liu, Yanlong, Zhang, Xiuhua, Hu, Lufeng
Format:	Online
Language:	English
Published:	Public Library of Science 2015
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4658146/

id	pubmed-4658146
recordtype	oai_dc
spelling	pubmed-46581462015-12-02 Using Blood Indexes to Predict Overweight Statuses: An Extreme Learning Machine-Based Approach Chen, Huiling Yang, Bo Liu, Dayou Liu, Wenbin Liu, Yanlong Zhang, Xiuhua Hu, Lufeng Research Article 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. Public Library of Science 2015-11-23 /pmc/articles/PMC4658146/ /pubmed/26600199 http://dx.doi.org/10.1371/journal.pone.0143003 Text en © 2015 Chen et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Chen, Huiling Yang, Bo Liu, Dayou Liu, Wenbin Liu, Yanlong Zhang, Xiuhua Hu, Lufeng
spellingShingle	Chen, Huiling Yang, Bo Liu, Dayou Liu, Wenbin Liu, Yanlong Zhang, Xiuhua Hu, Lufeng Using Blood Indexes to Predict Overweight Statuses: An Extreme Learning Machine-Based Approach
author_facet	Chen, Huiling Yang, Bo Liu, Dayou Liu, Wenbin Liu, Yanlong Zhang, Xiuhua Hu, Lufeng
author_sort	Chen, Huiling
title	Using Blood Indexes to Predict Overweight Statuses: An Extreme Learning Machine-Based Approach
title_short	Using Blood Indexes to Predict Overweight Statuses: An Extreme Learning Machine-Based Approach
title_full	Using Blood Indexes to Predict Overweight Statuses: An Extreme Learning Machine-Based Approach
title_fullStr	Using Blood Indexes to Predict Overweight Statuses: An Extreme Learning Machine-Based Approach
title_full_unstemmed	Using Blood Indexes to Predict Overweight Statuses: An Extreme Learning Machine-Based Approach
title_sort	using blood indexes to predict overweight statuses: an extreme learning machine-based approach
description	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.
publisher	Public Library of Science
publishDate	2015
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4658146/
_version_	1613505504929120256

Using Blood Indexes to Predict Overweight Statuses: An Extreme Learning Machine-Based Approach

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