- author = "Yolanda S. Baker and Rajeev Agrawal and James A. Foster and Daniel Beck and Gerry Dozier",
- title = "Applying machine learning techniques in detecting Bacterial Vaginosis",
- booktitle = "2014 International Conference on Machine Learning and Cybernetics",
- year = "2014",
- volume = "1",
- pages = "241--246",
- address = "Lanzhou, China",
- month = "13-16 " # jul,
- publisher = "IEEE",
- keywords = "genetic algorithms, genetic programming, Bacterial Vaginosis, Machine learning, Feature selection, Classification",
- isbn13 = "978-1-4799-4216-9",
-
DOI = "
doi:10.1109/ICMLC.2014.7009123",
- size = "6 pages",
- abstract = "There are several diseases which arise because of changes in the microbial communities in the body. Scientists continue to conduct research in a quest to find the catalysts that provoke these changes in the naturally occurring microbiota. Bacterial Vaginosis (BY) is a disease that fits the above criteria. BV afflicts approximately 29percent of women in child bearing age. Unfortunately, its causes are unknown. This paper seeks to uncover the most important features for diagnosis and in turn employ classification algorithms on those features. In order to fulfill our purpose, we conducted two experiments on the data. We isolated the clinical and medical features from the full set of raw data, we compared the accuracy, precision, recall and F-measure and time elapsed for each feature selection and classification grouping. We noticed that classification results were as good or better after performing feature selection although there was a wide range in the number of features produced from the feature selection process. After comparing the experiments, the algorithms performed best on the medical dataset.",
-
notes = "Weka, Java.
PubMed ID: 25914861",
