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Evaluating the Risk of Metabolic Syndrome Based on an Artificial Intelligence Model

Author

Listed:
  • Hui Chen
  • Shenghua Xiong
  • Xuan Ren

Abstract

Metabolic syndrome is worldwide public health problem and is a serious threat to people′s health and lives. Understanding the relationship between metabolic syndrome and the physical symptoms is a difficult and challenging task, and few studies have been performed in this field. It is important to classify adults who are at high risk of metabolic syndrome without having to use a biochemical index and, likewise, it is important to develop technology that has a high economic rate of return to simplify the complexity of this detection. In this paper, an artificial intelligence model was developed to identify adults at risk of metabolic syndrome based on physical signs; this artificial intelligence model achieved more powerful capacity for classification compared to the PCLR (principal component logistic regression) model. A case study was performed based on the physical signs data, without using a biochemical index, that was collected from the staff of Lanzhou Grid Company in Gansu province of China. The results show that the developed artificial intelligence model is an effective classification system for identifying individuals at high risk of metabolic syndrome.

Suggested Citation

  • Hui Chen & Shenghua Xiong & Xuan Ren, 2014. "Evaluating the Risk of Metabolic Syndrome Based on an Artificial Intelligence Model," Abstract and Applied Analysis, John Wiley & Sons, vol. 2014(1).
    • Handle: RePEc:wly:jnlaaa:v:2014:y:2014:i:1:n:207268
    DOI: 10.1155/2014/207268
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    References listed on IDEAS

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    1. Wen-Hsien Ho & King-Teh Lee & Hong-Yaw Chen & Te-Wei Ho & Herng-Chia Chiu, 2012. "Disease-Free Survival after Hepatic Resection in Hepatocellular Carcinoma Patients: A Prediction Approach Using Artificial Neural Network," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-9, January.
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