title: Detecting Bacterial Vaginosis Using Machine Learning.
abstract: Bacterial Vaginosis (BV) is the most common of vaginal infections diagnosed among women during the years where they can bear children. Yet, there is very little insight as to how it occurs. There are a vast number of criteria that can be taken into consideration to determine the presence of BV. The purpose of this paper is two-fold; first to discover the most significant features necessary to diagnose the infection, second is to apply various classification algorithms on the selected features. It is observed that certain feature selection algorithms provide only a few features; however, the classification results are as good as using a large number of features.
authors.affiliation: North Carolina A&T State University, 1601 E. Market St, Greensboro, NC 27411, ysbaker@aggies.ncat.edu.
authors.forename: Yolanda S
authors.initials: YS
authors.lastname: Baker
authors.affiliation: North Carolina A&T State University, 1601 E. Market St, Greensboro, NC 27411, ragrawal@ncat.edu.
authors.forename: Rajeev
authors.initials: R
authors.lastname: Agrawal
authors.affiliation: University of Idaho, 709 S. Deakin St, Moscow, ID 83844, foster@uidaho.edu.
authors.forename: James A
authors.initials: JA
authors.lastname: Foster
authors.affiliation: University of Idaho, 709 S Deakin St, Moscow, ID 83844, danlbek@gmail.com.
authors.forename: Daniel
authors.initials: D
authors.lastname: Beck
authors.affiliation: North Carolina A&T State University, 1601 E. Market St, Greensboro, NC 27411, gvdozier@ncat.edu.
authors.forename: Gerry
authors.initials: G
authors.lastname: Dozier
keywords: Algorithms; Classification; Feature Selection; Machine Learning