Journal Article

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

Santana, Marília, de Morais Melo, Flávio Leandro, de Morais, Cleber Matos, Teixeira, Igor Vitor, da Silva Leite, Morgana Thalita, Endo, Patricia Takako, Vieira Gadelha, Keduly, Sadok, Sara, Pinheiro Rodrigues, Cristina, de Lima Oliveira, Ana Maria, Pessoa da Costa Carrarine, Ana Sofia, Kelner, Judith, da Silva Rocha, Élisson

2023

Overview

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. 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 health environment. We use clinical and sociodemographic data from pregnant women that were assisted by a social program in Pernambuco, Brazil, named Mãe Coruja Pernambucana Program (PMCP). Based on a rigorous methodology, we propose six experiments using three feature selection techniques to select the most relevant attributes, pre-process and clean the data, apply hyperparameter optimization to tune the machine learning models, and train and test models to have a fair evaluation and discussion. The AdaBoost-BODS-Expert model, an Adaptive Boosting (AdaBoost) model that used attributes selected by health experts, presented the best results in terms of evaluation metrics and acceptance by health experts from PMCP. By using this model, the results are more reliable and allows adoption on a daily usage to classify possible outcomes of congenital syphilis using clinical and sociodemographic data.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Acceptance tests / Biology and Life Sciences / Births / Brazil - epidemiology / Child / Child, Preschool / Computer and Information Sciences / Congenital diseases / Developing countries / Diagnosis / Engineering and Technology / Environmental factors / Epidemiological Monitoring / Epidemiology / Health care / Humans / Incidence / Infections / LDCs / Learning algorithms / Machine Learning / Medical personnel / Medicine and Health Sciences / Modelling / Optimization / Performance evaluation / Pregnancy / Prenatal care / Public health / Research and Analysis Methods / Resource allocation / Risk factors / Sexually transmitted diseases / Social Sciences / Sociodemographic Factors / Sociodemographics / STD / Syphilis / Syphilis, Congenital - classification / Syphilis, Congenital - epidemiology / Trends / Womens health