Detecting Bacterial Vaginosis Using Machine Learning

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...

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Veröffentlicht in:Proceedings of the 2014 ACM Southeast Regional Conference Jg. 2014
Hauptverfasser: Baker, Yolanda S, Agrawal, Rajeev, Foster, James A, Beck, Daniel, Dozier, Gerry
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 01.03.2014
Schlagworte:
Feature Selection
Algorithms
Machine Learning
Classification
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Zusammenfassung: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.
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DOI:10.1145/2638404.2638521