Comparison of classification algorithms with wrapper-based feature selection for predicting osteoporosis outcome based on genetic factors in a Taiwanese women population

Hsueh Wei Chang, Yu Hsien Chiu, Hao Yun Kao, Cheng Hong Yang, Wen Hsien Ho

研究成果: Article

14 引文 斯高帕斯(Scopus)

摘要

An essential task in a genomic analysis of a human disease is limiting the number of strongly associated genes when studying susceptibility to the disease. The goal of this study was to compare computational tools with and without feature selection for predicting osteoporosis outcome in Taiwanese women based on genetic factors such as single nucleotide polymorphisms (SNPs). To elucidate relationships between osteoporosis and SNPs in this population, three classification algorithms were applied: multilayer feedforward neural network (MFNN), naive Bayes, and logistic regression. A wrapper-based feature selection method was also used to identify a subset of major SNPs. Experimental results showed that the MFNN model with the wrapper-based approach was the best predictive model for inferring disease susceptibility based on the complex relationship between osteoporosis and SNPs in Taiwanese women. The findings suggest that patients and doctors can use the proposed tool to enhance decision making based on clinical factors such as SNP genotyping data.

原文English
文章編號850735
期刊International Journal of Endocrinology
2013
DOIs
出版狀態Published - 2013

All Science Journal Classification (ASJC) codes

  • Endocrinology, Diabetes and Metabolism
  • Endocrinology
  • Endocrine and Autonomic Systems

指紋 深入研究「Comparison of classification algorithms with wrapper-based feature selection for predicting osteoporosis outcome based on genetic factors in a Taiwanese women population」主題。共同形成了獨特的指紋。

  • 引用此