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Genetic Algorithm Based Methods for Identification of Health Risk Factors Aimed at Preventing Metabolic Syndrome

  • Topon Kumar Paul
  • Ken Ueno
  • Koichiro Iwata
  • Toshio Hayashi
  • Nobuyoshi Honda
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5361)

Abstract

In recent years, metabolic syndrome has emerged as a major health concern because it increases the risk of developing lifestyle diseases, such as diabetes, hypertension, and cardiovascular disease. Some of the symptoms of the metabolic syndrome are high blood pressure, decreased HDL cholesterol, and elevated triglycerides (TG). To prevent the developing of metabolic syndrome, accurate prediction of the future values of these health risk factors and identification of other factors from the health checkup and lifestyle data, which are highly related with these risk factors, are very important. In this paper, we propose a new framework, based on genetic algorithm and its variants, for identifying those important health factors and predicting the future health risk of a person with high accuracy. We show the effectiveness of the proposed system by applying it to the health checkup and lifestyle data of Toshiba Corporation.

Keywords

Feature selection classification unbalanced data metabolic syndrome fitness evaluation RPMBGA+ AUC balanced 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Topon Kumar Paul
    • 1
  • Ken Ueno
    • 1
  • Koichiro Iwata
    • 2
  • Toshio Hayashi
    • 2
  • Nobuyoshi Honda
    • 2
  1. 1.Corporate Research & Development Center, Toshiba CorporationKawasakiJapan
  2. 2.Toshiba CorporationTokyoJapan

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