Predicting congenital syphilis cases: A performance evaluation of different machine learning models.

ABSTRACT:

Background

Communicable diseases represent a huge economic burden for healthcare systems and for society. Sexually transmitted infections (STIs) are a concerning issue, especially in developing and underdeveloped countries, in which environmental factors and other determinants of health play a role in contributing to its fast spread. In light of this situation, machine learning techniques have been explored to assess the incidence of syphilis and contribute to the epidemiological surveillance in this scenario.

Objective

The main goal of this work is to evaluate the performance of different machine learning models on predicting undesirable outcomes of congenital syphilis in order to assist resources allocation and optimize the healthcare actions, especially in a constrained h

SUBMITTER: Teixeira IV 

PROVIDER: S-EPMC10237406 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature