Literature DB >> 20619912

Identification of metabolic syndrome using decision tree analysis.

Apilak Worachartcheewan1, Chanin Nantasenamat, Chartchalerm Isarankura-Na-Ayudhya, Phannee Pidetcha, Virapong Prachayasittikul.   

Abstract

This study employs decision tree as a decision support system for rapid and automated identification of individuals with metabolic syndrome (MS) among a Thai population. Results demonstrated strong predictivity of the decision tree in classification of individuals with and without MS, displaying an overall accuracy in excess of 99%.

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Year:  2010        PMID： 20619912     DOI： 10.1016/j.diabres.2010.06.009

Source DB:  PubMed          Journal:  Diabetes Res Clin Pract        ISSN： 0168-8227            Impact factor:   5.602


  19 in total

1.  PyBact: an algorithm for bacterial identification.

Authors:  Chanin Nantasenamat; Likit Preeyanon; Chartchalerm Isarankura-Na-Ayudhya; Virapong Prachayasittikul
Journal:  EXCLI J       Date:  2011-11-24       Impact factor: 4.068

2.  A decision tree-based approach for identifying urban-rural differences in metabolic syndrome risk factors in the adult Korean population.

Authors:  T N Kim; J M Kim; J C Won; M S Park; S K Lee; S H Yoon; H-R Kim; K S Ko; B D Rhee
Journal:  J Endocrinol Invest       Date:  2012-01-30       Impact factor: 4.256

3.  Exploring the chemical space of aromatase inhibitors.

Authors:  Chanin Nantasenamat; Hao Li; Prasit Mandi; Apilak Worachartcheewan; Teerawat Monnor; Chartchalerm Isarankura-Na-Ayudhya; Virapong Prachayasittikul
Journal:  Mol Divers       Date:  2013-07-16       Impact factor: 2.943

4.  Large-scale structure-activity relationship study of hepatitis C virus NS5B polymerase inhibition using SMILES-based descriptors.

Authors:  Apilak Worachartcheewan; Virapong Prachayasittikul; Alla P Toropova; Andrey A Toropov; Chanin Nantasenamat
Journal:  Mol Divers       Date:  2015-11       Impact factor: 2.943

5.  Predicting Metabolic Syndrome Using the Random Forest Method.

Authors:  Apilak Worachartcheewan; Watshara Shoombuatong; Phannee Pidetcha; Wuttichai Nopnithipat; Virapong Prachayasittikul; Chanin Nantasenamat
Journal:  ScientificWorldJournal       Date:  2015-07-28

6.  Quantitative population-health relationship (QPHR) for assessing metabolic syndrome.

Authors:  Apilak Worachartcheewan; Chanin Nantasenamat; Chartchalerm Isarankura-Na-Ayudhya; Virapong Prachayasittikul
Journal:  EXCLI J       Date:  2013-06-26       Impact factor: 4.068

7.  Applying data mining techniques to improve diagnosis in neonatal jaundice.

Authors:  Duarte Ferreira; Abílio Oliveira; Alberto Freitas
Journal:  BMC Med Inform Decis Mak       Date:  2012-12-07       Impact factor: 2.796

8.  Use of CHAID decision trees to formulate pathways for the early detection of metabolic syndrome in young adults.

Authors:  Brian Miller; Mark Fridline; Pei-Yang Liu; Deborah Marino
Journal:  Comput Math Methods Med       Date:  2014-04-10       Impact factor: 2.238

9.  Machine learning approaches for discerning intercorrelation of hematological parameters and glucose level for identification of diabetes mellitus.

Authors:  Apilak Worachartcheewan; Chanin Nantasenamat; Pisit Prasertsrithong; Jakraphob Amranan; Teerawat Monnor; Tassaneya Chaisatit; Wilairat Nuchpramool; Virapong Prachayasittikul
Journal:  EXCLI J       Date:  2013-10-21       Impact factor: 4.068

10.  Predicting metabolic syndrome using decision tree and support vector machine methods.

Authors:  Farzaneh Karimi-Alavijeh; Saeed Jalili; Masoumeh Sadeghi
Journal:  ARYA Atheroscler       Date:  2016-05
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