# Predictor Selection for Bacterial Vaginosis Diagnosis Using Decision Tree and Relief Algorithms
###### Abstract  
Requiring only a few relevant characteristics from patients when diagnosing bacterial vagnosis is highly useful for physicians as it makes it less time consuming to collect these data. This would result in having a dataset of patients that can be more accurately diagnosed using only a subset of informative or relevant features in contrast to using the entire set of features. As such, this is a feature selection (FS) problem. In this work, decision tree and Relief algorithms were used as feature selectors. Experiments were conducted on a real dataset for bacterial vagnosis with 396 instances and 252 features/attributes. The dataset was obtained from universities located in Baltimore and Atlanta. The FS algorithms utilized feature rankings, from which the top fifteen features formed a new dataset that was used as input for both support vector machine (SVM) and logistic regression (LR) algorithms for classification. For performance evaluation, averages of 30 runs of 10-fold cross-validation were reported, along with balanced accuracy, sensitivity, and specificity as performance measures. A performance comparison of the results was made between using the total number of features against using the top fifteen. These results found similar attributes from our rankings compared to those reported in the literature. This study is part of ongoing research that is investigating a range of feature selection and classification methods. feature selection; bacterial vagnosis diagnosis; microbial communities; SVM; logistic regression +
Footnote †: journal: Medicalstudies) to obtain to rule out diagnoses in the differential diagnosis set. Through hypothetic-deductive reasoning, the most useful features are identified such that when the probability of one of the diagnoses reaches a level of acceptability, the process is stopped and the diagnosis is accepted. It may be possible to achieve an acceptable level of certainty of the diagnosis with only a few features and thus not need to process the entire feature set. Therefore, the physician can be considered a classifier of sorts. Machine learning algorithms (MLAs) based on artificial intelligence (AI) methods can also do this. From this approach, the objectives and motivations of this work were established: first, using MLAs to explore the most relevant attributes in the data of BV that minimizes the needed data to diagnose it, and second, to explore the features or feature subsets that positively impact the performance of the classification algorithms (CAs) regarding diagnosing BV. For this reason, this work was addressed as a feature selection problem. Feature selection (FS) involves the reduction of the number of features (attributes, variables, or predictors) for each instance in a dataset, discarding the less important attributes [5]. The main objectives of FS involve improving the prediction performance of the classifiers, providing faster and more cost-effective predictors, and providing a better understanding of the underlying process that generated the data [6]. The support vector machine (SVM) and logistic regression (LR) classifiers were used in this study to measure the ability to classify patients either as positive or negative cases of BV by obtaining the balanced accuracy, sensitivity, and specificity performance measures. The dataset used for this objective was obtained from universities located in Baltimore and Atlanta. It contained clinical and biological information about vaginal microorganisms. Finally, the contribution made by this research can be summarized as follows:  
1. The determination of the best fifteen predictors for bacterial vaginosis diagnosis using feature selection algorithms. 2. Comparison of the results obtained in this research to those obtained in [PERSON] and [PERSON] [7]. 3. The determination of a highly promising combination of SVM as a classification algorithm and decision trees as a feature selector for bacterial vaginosis diagnosis.