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

Huiling Chen, Bo Yang, Dayou Liu, Wenbin Liu, Yanlong Liu, Xiuhua Zhang and Lufeng Hu

PLOS ONE, 2015, vol. 10, issue 11, 1-18

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

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0143003

DOI: 10.1371/journal.pone.0143003

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