Comparison of three data mining models for prediction of advanced schistosomiasis prognosis in the Hubei province.
In order to better assist medical professionals, this study aimed to develop and compare the performance of three models-a multivariate logistic regression (LR) model, an artificial neural network (ANN) model, and a decision tree (DT) model-to predict the prognosis of patients with advanced schistos...
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ftdoajarticles:oai:doaj.org/article:2a3ba7f03dea4272b497523940fb647d 2023-05-15T15:11:28+02:00 Comparison of three data mining models for prediction of advanced schistosomiasis prognosis in the Hubei province. Guo Li Xiaorong Zhou Jianbing Liu Yuanqi Chen Hengtao Zhang Yanyan Chen Jianhua Liu Hongbo Jiang Junjing Yang Shaofa Nie 2018-02-01T00:00:00Z https://doi.org/10.1371/journal.pntd.0006262 https://doaj.org/article/2a3ba7f03dea4272b497523940fb647d EN eng Public Library of Science (PLoS) http://europepmc.org/articles/PMC5831639?pdf=render https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0006262 https://doaj.org/article/2a3ba7f03dea4272b497523940fb647d PLoS Neglected Tropical Diseases, Vol 12, Iss 2, p e0006262 (2018) Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 article 2018 ftdoajarticles https://doi.org/10.1371/journal.pntd.0006262 2022-12-31T04:37:56Z In order to better assist medical professionals, this study aimed to develop and compare the performance of three models-a multivariate logistic regression (LR) model, an artificial neural network (ANN) model, and a decision tree (DT) model-to predict the prognosis of patients with advanced schistosomiasis residing in the Hubei province.Schistosomiasis surveillance data were collected from a previous study based on a Hubei population sample including 4136 advanced schistosomiasis cases. The predictive models use LR, ANN, and DT methods. From each of the three groups, 70% of the cases (2896 cases) were used as training data for the predictive models. The remaining 30% of the cases (1240 cases) were used as validation groups for performance comparisons between the three models. Prediction performance was evaluated using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Univariate analysis indicated that 16 risk factors were significantly associated with a patient's outcome of prognosis. In the training group, the mean AUC was 0.8276 for LR, 0.9267 for ANN, and 0.8229 for DT. In the validation group, the mean AUC was 0.8349 for LR, 0.8318 for ANN, and 0.8148 for DT. The three models yielded similar results in terms of accuracy, sensitivity, and specificity.Predictive models for advanced schistosomiasis prognosis, respectively using LR, ANN and DT models were proved to be effective approaches based on our dataset. The ANN model outperformed the LR and DT models in terms of AUC. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic PLOS Neglected Tropical Diseases 12 2 e0006262 |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
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English |
topic |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 Guo Li Xiaorong Zhou Jianbing Liu Yuanqi Chen Hengtao Zhang Yanyan Chen Jianhua Liu Hongbo Jiang Junjing Yang Shaofa Nie Comparison of three data mining models for prediction of advanced schistosomiasis prognosis in the Hubei province. |
topic_facet |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
description |
In order to better assist medical professionals, this study aimed to develop and compare the performance of three models-a multivariate logistic regression (LR) model, an artificial neural network (ANN) model, and a decision tree (DT) model-to predict the prognosis of patients with advanced schistosomiasis residing in the Hubei province.Schistosomiasis surveillance data were collected from a previous study based on a Hubei population sample including 4136 advanced schistosomiasis cases. The predictive models use LR, ANN, and DT methods. From each of the three groups, 70% of the cases (2896 cases) were used as training data for the predictive models. The remaining 30% of the cases (1240 cases) were used as validation groups for performance comparisons between the three models. Prediction performance was evaluated using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Univariate analysis indicated that 16 risk factors were significantly associated with a patient's outcome of prognosis. In the training group, the mean AUC was 0.8276 for LR, 0.9267 for ANN, and 0.8229 for DT. In the validation group, the mean AUC was 0.8349 for LR, 0.8318 for ANN, and 0.8148 for DT. The three models yielded similar results in terms of accuracy, sensitivity, and specificity.Predictive models for advanced schistosomiasis prognosis, respectively using LR, ANN and DT models were proved to be effective approaches based on our dataset. The ANN model outperformed the LR and DT models in terms of AUC. |
format |
Article in Journal/Newspaper |
author |
Guo Li Xiaorong Zhou Jianbing Liu Yuanqi Chen Hengtao Zhang Yanyan Chen Jianhua Liu Hongbo Jiang Junjing Yang Shaofa Nie |
author_facet |
Guo Li Xiaorong Zhou Jianbing Liu Yuanqi Chen Hengtao Zhang Yanyan Chen Jianhua Liu Hongbo Jiang Junjing Yang Shaofa Nie |
author_sort |
Guo Li |
title |
Comparison of three data mining models for prediction of advanced schistosomiasis prognosis in the Hubei province. |
title_short |
Comparison of three data mining models for prediction of advanced schistosomiasis prognosis in the Hubei province. |
title_full |
Comparison of three data mining models for prediction of advanced schistosomiasis prognosis in the Hubei province. |
title_fullStr |
Comparison of three data mining models for prediction of advanced schistosomiasis prognosis in the Hubei province. |
title_full_unstemmed |
Comparison of three data mining models for prediction of advanced schistosomiasis prognosis in the Hubei province. |
title_sort |
comparison of three data mining models for prediction of advanced schistosomiasis prognosis in the hubei province. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2018 |
url |
https://doi.org/10.1371/journal.pntd.0006262 https://doaj.org/article/2a3ba7f03dea4272b497523940fb647d |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
PLoS Neglected Tropical Diseases, Vol 12, Iss 2, p e0006262 (2018) |
op_relation |
http://europepmc.org/articles/PMC5831639?pdf=render https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0006262 https://doaj.org/article/2a3ba7f03dea4272b497523940fb647d |
op_doi |
https://doi.org/10.1371/journal.pntd.0006262 |
container_title |
PLOS Neglected Tropical Diseases |
container_volume |
12 |
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2 |
container_start_page |
e0006262 |
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