Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Preliminary Results to Predict Tuberculosis Outcomes Applying Traditional and Automated Machine Learning Models
5
Zitationen
10
Autoren
2023
Jahr
Abstract
Tuberculosis (TB) remains one of the most lethal infectious diseases in the world and, despite being preventable and curable, kills 4.500 people daily, according to the World Health Organization (WHO). Brazil, being a country heavily affected by TB, works to improve social intervention programs, since the decrease in the patients vulnerability seems to have a positive effect for the cure of TB. The Brazilian public health system records data on TB treatment that can guide actions and interventions. In this context, machine learning (ML) algorithms have been used successfully to analyze health and medicine (H&M) datasets. An emerging area of ML called Automated Machine Learning (Auto-ML) was tested in this analysis to predict the following TB results: good and bad outcomes. Our results indicate that it is possible to build reasonable ML models with the available data.
Ähnliche Arbeiten
Biostatistical Analysis
1996 · 35.449 Zit.
UCI Machine Learning Repository
2007 · 24.319 Zit.
An introduction to ROC analysis
2005 · 20.831 Zit.
The use of the area under the ROC curve in the evaluation of machine learning algorithms
1997 · 7.156 Zit.
A method of comparing the areas under receiver operating characteristic curves derived from the same cases.
1983 · 7.076 Zit.