Predictor Selection for Bacterial Vaginosis Diagnosis Using Decision Tree and Relief Algorithms

Predictor Selection for Bacterial Vaginosis Diagnosis Using Decision Tree and Relief Algorithms

Requiring only a few relevant characteristics from patients when diagnosing bacterial vaginosis 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 inf...

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Bibliographic Details
Main Authors: Jesús F. Pérez-Gómez, Juana Canul-Reich, José Hernández-Torruco, Betania Hernández-Ocaña
Format: Article
Language:English
Published: MDPI AG 2020-05-01
Series:Applied Sciences
Subjects:
feature selection
bacterial vaginosis diagnosis
microbial communities
SVM
logistic regression
Online Access:https://www.mdpi.com/2076-3417/10/9/3291

Internet

https://www.mdpi.com/2076-3417/10/9/3291

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