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Machine Learning Model for Imbalanced Cholera Dataset in Tanzania

Cholera epidemic remains a public threat throughout history, affecting vulnerable population living with unreliable water and substandard sanitary conditions. Various studies have observed that the occurrence of cholera has strong linkage with environmental factors such as climate change and geograp...

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Autores principales: Leo, Judith, Luhanga, Edith, Michael, Kisangiri
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6683776/
https://www.ncbi.nlm.nih.gov/pubmed/31427903
http://dx.doi.org/10.1155/2019/9397578
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author Leo, Judith
Luhanga, Edith
Michael, Kisangiri
author_facet Leo, Judith
Luhanga, Edith
Michael, Kisangiri
author_sort Leo, Judith
collection PubMed
description Cholera epidemic remains a public threat throughout history, affecting vulnerable population living with unreliable water and substandard sanitary conditions. Various studies have observed that the occurrence of cholera has strong linkage with environmental factors such as climate change and geographical location. Climate change has been strongly linked to the seasonal occurrence and widespread of cholera through the creation of weather patterns that favor the disease's transmission, infection, and the growth of Vibrio cholerae, which cause the disease. Over the past decades, there have been great achievements in developing epidemic models for the proper prediction of cholera. However, the integration of weather variables and use of machine learning techniques have not been explicitly deployed in modeling cholera epidemics in Tanzania due to the challenges that come with its datasets such as imbalanced data and missing information. This paper explores the use of machine learning techniques to model cholera epidemics with linkage to seasonal weather changes while overcoming the data imbalance problem. Adaptive Synthetic Sampling Approach (ADASYN) and Principal Component Analysis (PCA) were used to the restore sampling balance and dimensional of the dataset. In addition, sensitivity, specificity, and balanced-accuracy metrics were used to evaluate the performance of the seven models. Based on the results of the Wilcoxon sign-rank test and features of the models, XGBoost classifier was selected to be the best model for the study. Overall results improved our understanding of the significant roles of machine learning strategies in health-care data. However, the study could not be treated as a time series problem due to the data collection bias. The study recommends a review of health-care systems in order to facilitate quality data collection and deployment of machine learning techniques.
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spelling pubmed-66837762019-08-19 Machine Learning Model for Imbalanced Cholera Dataset in Tanzania Leo, Judith Luhanga, Edith Michael, Kisangiri ScientificWorldJournal Research Article Cholera epidemic remains a public threat throughout history, affecting vulnerable population living with unreliable water and substandard sanitary conditions. Various studies have observed that the occurrence of cholera has strong linkage with environmental factors such as climate change and geographical location. Climate change has been strongly linked to the seasonal occurrence and widespread of cholera through the creation of weather patterns that favor the disease's transmission, infection, and the growth of Vibrio cholerae, which cause the disease. Over the past decades, there have been great achievements in developing epidemic models for the proper prediction of cholera. However, the integration of weather variables and use of machine learning techniques have not been explicitly deployed in modeling cholera epidemics in Tanzania due to the challenges that come with its datasets such as imbalanced data and missing information. This paper explores the use of machine learning techniques to model cholera epidemics with linkage to seasonal weather changes while overcoming the data imbalance problem. Adaptive Synthetic Sampling Approach (ADASYN) and Principal Component Analysis (PCA) were used to the restore sampling balance and dimensional of the dataset. In addition, sensitivity, specificity, and balanced-accuracy metrics were used to evaluate the performance of the seven models. Based on the results of the Wilcoxon sign-rank test and features of the models, XGBoost classifier was selected to be the best model for the study. Overall results improved our understanding of the significant roles of machine learning strategies in health-care data. However, the study could not be treated as a time series problem due to the data collection bias. The study recommends a review of health-care systems in order to facilitate quality data collection and deployment of machine learning techniques. Hindawi 2019-07-25 /pmc/articles/PMC6683776/ /pubmed/31427903 http://dx.doi.org/10.1155/2019/9397578 Text en Copyright © 2019 Judith Leo et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Leo, Judith
Luhanga, Edith
Michael, Kisangiri
Machine Learning Model for Imbalanced Cholera Dataset in Tanzania
title Machine Learning Model for Imbalanced Cholera Dataset in Tanzania
title_full Machine Learning Model for Imbalanced Cholera Dataset in Tanzania
title_fullStr Machine Learning Model for Imbalanced Cholera Dataset in Tanzania
title_full_unstemmed Machine Learning Model for Imbalanced Cholera Dataset in Tanzania
title_short Machine Learning Model for Imbalanced Cholera Dataset in Tanzania
title_sort machine learning model for imbalanced cholera dataset in tanzania
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6683776/
https://www.ncbi.nlm.nih.gov/pubmed/31427903
http://dx.doi.org/10.1155/2019/9397578
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