Detecting Bacterial Vaginosis Using Machine Learning

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@InProceedings{Baker:2014:DBV:2638404.2638521,

• author = "Yolanda S. Baker and Rajeev Agrawal and James A. Foster and Daniel Beck and Gerry Dozier",
• title = "Detecting Bacterial Vaginosis Using Machine Learning",
• booktitle = "Proceedings of the 2014 ACM Southeast Regional Conference",
• year = "2014",
• pages = "46:1--46:4",
• address = "Kennesaw, Georgia, USA",
• month = mar # " 28-29",
• publisher = "ACM",
• keywords = "genetic algorithms, genetic programming",
• acmid = "2638521",
• isbn13 = "978-1-4503-2923-1",
• DOI = "doi:10.1145/2638404.2638521",
• size = "4 pages",
• 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.",
• notes = "ACM SE '14",

}

Genetic Programming entries for Yolanda S Baker Rajeev Agrawal James A Foster Daniel Beck Gerry Dozier

Citations