Two-year death prediction models among patients with Chagas Disease using machine learning-based methods
Chagas disease (CD) is recognized by the World Health Organization as one of the thirteen most neglected tropical diseases. More than 80% of people affected by CD will not have access to diagnosis and continued treatment, which partly supports the high morbidity and mortality rate. Machine Learning...
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crplos:10.1371/journal.pntd.0010356 2024-10-13T14:10:37+00:00 Two-year death prediction models among patients with Chagas Disease using machine learning-based methods Ferreira, Ariela Mota Santos, Laércio Ives Sabino, Ester Cerdeira Ribeiro, Antonio Luiz Pinho Oliveira-da Silva, Léa Campos de Damasceno, Renata Fiúza D’Angelo, Marcos Flávio Silveira Vasconcelos Nunes, Maria do Carmo Pereira Haikal, Desirée Sant´Ana Ramos, Alberto Novaes National Institute of Health National Institute of Health 2022 http://dx.doi.org/10.1371/journal.pntd.0010356 https://dx.plos.org/10.1371/journal.pntd.0010356 en eng Public Library of Science (PLoS) http://creativecommons.org/licenses/by/4.0/ PLOS Neglected Tropical Diseases volume 16, issue 4, page e0010356 ISSN 1935-2735 journal-article 2022 crplos https://doi.org/10.1371/journal.pntd.0010356 2024-10-01T04:05:11Z Chagas disease (CD) is recognized by the World Health Organization as one of the thirteen most neglected tropical diseases. More than 80% of people affected by CD will not have access to diagnosis and continued treatment, which partly supports the high morbidity and mortality rate. Machine Learning (ML) can identify patterns in data that can be used to increase our understanding of a specific problem or make predictions about the future. Thus, the aim of this study was to evaluate different models of ML to predict death in two years of patients with CD. ML models were developed using different techniques and configurations. The techniques used were: Random Forests, Adaptive Boosting, Decision Tree, Support Vector Machine, and Artificial Neural Networks. The adopted settings considered only interview variables, only complementary exam variables, and finally, both mixed. Data from a cohort study with CD patients called SaMi-Trop were analyzed. The predictor variables came from the baseline; and the outcome, which was death, came from the first follow-up. All models were evaluated in terms of Sensitivity, Specificity and G-mean. Among the 1694 individuals with CD considered, 134 (7.9%) died within two years of follow-up. Using only the predictor variables from the interview, the different techniques achieved a maximum G-mean of 0.64 in predicting death. Using only the variables from complementary exams, the G-mean was up to 0.77. In this configuration, the protagonism of NT-proBNP was evident, where it was possible to observe that an ML model using only this single variable reached G-mean of 0.76. The configuration that mixed interview variables and complementary exams achieved G-mean of 0.75. ML can be used as a useful tool with the potential to contribute to the management of patients with CD, by identifying patients with the highest probability of death. Trial Registration: This trial is registered with ClinicalTrials.gov, Trial ID: NCT02646943 . Article in Journal/Newspaper sami PLOS PLOS Neglected Tropical Diseases 16 4 e0010356 |
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English |
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Chagas disease (CD) is recognized by the World Health Organization as one of the thirteen most neglected tropical diseases. More than 80% of people affected by CD will not have access to diagnosis and continued treatment, which partly supports the high morbidity and mortality rate. Machine Learning (ML) can identify patterns in data that can be used to increase our understanding of a specific problem or make predictions about the future. Thus, the aim of this study was to evaluate different models of ML to predict death in two years of patients with CD. ML models were developed using different techniques and configurations. The techniques used were: Random Forests, Adaptive Boosting, Decision Tree, Support Vector Machine, and Artificial Neural Networks. The adopted settings considered only interview variables, only complementary exam variables, and finally, both mixed. Data from a cohort study with CD patients called SaMi-Trop were analyzed. The predictor variables came from the baseline; and the outcome, which was death, came from the first follow-up. All models were evaluated in terms of Sensitivity, Specificity and G-mean. Among the 1694 individuals with CD considered, 134 (7.9%) died within two years of follow-up. Using only the predictor variables from the interview, the different techniques achieved a maximum G-mean of 0.64 in predicting death. Using only the variables from complementary exams, the G-mean was up to 0.77. In this configuration, the protagonism of NT-proBNP was evident, where it was possible to observe that an ML model using only this single variable reached G-mean of 0.76. The configuration that mixed interview variables and complementary exams achieved G-mean of 0.75. ML can be used as a useful tool with the potential to contribute to the management of patients with CD, by identifying patients with the highest probability of death. Trial Registration: This trial is registered with ClinicalTrials.gov, Trial ID: NCT02646943 . |
author2 |
Ramos, Alberto Novaes National Institute of Health National Institute of Health |
format |
Article in Journal/Newspaper |
author |
Ferreira, Ariela Mota Santos, Laércio Ives Sabino, Ester Cerdeira Ribeiro, Antonio Luiz Pinho Oliveira-da Silva, Léa Campos de Damasceno, Renata Fiúza D’Angelo, Marcos Flávio Silveira Vasconcelos Nunes, Maria do Carmo Pereira Haikal, Desirée Sant´Ana |
spellingShingle |
Ferreira, Ariela Mota Santos, Laércio Ives Sabino, Ester Cerdeira Ribeiro, Antonio Luiz Pinho Oliveira-da Silva, Léa Campos de Damasceno, Renata Fiúza D’Angelo, Marcos Flávio Silveira Vasconcelos Nunes, Maria do Carmo Pereira Haikal, Desirée Sant´Ana Two-year death prediction models among patients with Chagas Disease using machine learning-based methods |
author_facet |
Ferreira, Ariela Mota Santos, Laércio Ives Sabino, Ester Cerdeira Ribeiro, Antonio Luiz Pinho Oliveira-da Silva, Léa Campos de Damasceno, Renata Fiúza D’Angelo, Marcos Flávio Silveira Vasconcelos Nunes, Maria do Carmo Pereira Haikal, Desirée Sant´Ana |
author_sort |
Ferreira, Ariela Mota |
title |
Two-year death prediction models among patients with Chagas Disease using machine learning-based methods |
title_short |
Two-year death prediction models among patients with Chagas Disease using machine learning-based methods |
title_full |
Two-year death prediction models among patients with Chagas Disease using machine learning-based methods |
title_fullStr |
Two-year death prediction models among patients with Chagas Disease using machine learning-based methods |
title_full_unstemmed |
Two-year death prediction models among patients with Chagas Disease using machine learning-based methods |
title_sort |
two-year death prediction models among patients with chagas disease using machine learning-based methods |
publisher |
Public Library of Science (PLoS) |
publishDate |
2022 |
url |
http://dx.doi.org/10.1371/journal.pntd.0010356 https://dx.plos.org/10.1371/journal.pntd.0010356 |
genre |
sami |
genre_facet |
sami |
op_source |
PLOS Neglected Tropical Diseases volume 16, issue 4, page e0010356 ISSN 1935-2735 |
op_rights |
http://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1371/journal.pntd.0010356 |
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PLOS Neglected Tropical Diseases |
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16 |
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4 |
container_start_page |
e0010356 |
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1812817967088402432 |