A comparison of classification methods for predicting Chronic Fatigue Syndrome based on genetic data.

ABSTRACT:

Background

In the studies of genomics, it is essential to select a small number of genes that are more significant than the others for the association studies of disease susceptibility. In this work, our goal was to compare computational tools with and without feature selection for predicting chronic fatigue syndrome (CFS) using genetic factors such as single nucleotide polymorphisms (SNPs).

Methods

We employed the dataset that was original to the previous study by the CDC Chronic Fatigue Syndrome Research Group. To uncover relationships between CFS and SNPs, we applied three classification algorithms including naive Bayes, the support vector machine algorithm, and the C4.5 decision tree algorithm. Furthermore, we utilized feature selection methods to identify a subset of in

SUBMITTER: Huang LC 

PROVIDER: S-EPMC2765429 | biostudies-literature | 2009 Sep

REPOSITORIES: biostudies-literature