Applying machine learning techniques in detecting Bacterial Vaginosis

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@InProceedings{Baker:2014:ICMLC,
  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",
  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",
}

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

Citations