An Enhanced Method for Diagnosis of Bacterial Vaginosis based on Support Vector Machines with Linear Kernel

  • Jesus Francisco Pérez-Gómez Universidad Juarez Autonoma de Tabasco. Division Académica de Ciencias y Tecnologías de la Información
  • Juana Canul-Reich Autonomous Juárez University of Tabasco.
  • Erick De La Cruz-Hernandez Autonomous Juárez University of Tabasco
Keywords: Classification, bacterial vaginosis diagnosis, kernel-based method, reduction dimension, svm, feature selection

Abstract

Bacterial Vaginosis (BV) is a pathological condition that causes complications in women’s health. Efforts to characterize it have failed to reveal a BV etiology. In this work, the Support Vector Machine (SVM) is used as base classifier in three different scenarios to identify between classes of VB. The first scenario uses the entire feature set in the dataset. The second scenario uses two sub-datasets created with the features in two rankings obtained from previous work. The third scenario uses one feature at a time to create classifiers. Performance measures in each are given. The dataset used is a real vaginal microbiology test of 201 women from Tabasco, Mexico. Results show SVM surprisingly obtained 100% accuracy in a classifier made of a single feature. This research is a first effort to lay the groundwork for computer-based BV diagnosis as advice.

Author Biographies

Juana Canul-Reich, Autonomous Juárez University of Tabasco.

Full-time Professor investigator from the Computer Science area.

Autonomous Juárez University of Tabasco.

Academic Division of Information Sciences and Technologies

Erick De La Cruz-Hernandez, Autonomous Juárez University of Tabasco

Full-time research professor from microbiology area

Autonomous Juárez University of Tabasco

Comalcalco Multidisciplinary Academic Division

Published
2021-09-11
How to Cite
Pérez-Gómez, J. F., Canul-Reich, J., & De La Cruz-Hernandez, E. (2021). An Enhanced Method for Diagnosis of Bacterial Vaginosis based on Support Vector Machines with Linear Kernel. International Journal of Combinatorial Optimization Problems and Informatics, 12(3), 109-121. Retrieved from https://ijcopi.org/ojs/article/view/212
Section
Articles