Cargando…

Applications of machine learning techniques to predict filariasis using socio-economic factors

Filariasis is one of the major public health concerns in India. Approximately 600 million people spread across 250 districts of India are at risk of filariasis. To predict this disease, a pilot scale study was carried out in 30 villages of Karimnagar district of Telangana from 2004 to 2007 to collec...

Descripción completa

Detalles Bibliográficos
Autores principales: Kondeti, Phani Krishna, Ravi, Kumar, Mutheneni, Srinivasa Rao, Kadiri, Madhusudhan Rao, Kumaraswamy, Sriram, Vadlamani, Ravi, Upadhyayula, Suryanaryana Murty
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cambridge University Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6805759/
https://www.ncbi.nlm.nih.gov/pubmed/31475670
http://dx.doi.org/10.1017/S0950268819001481
_version_ 1783461468148072448
author Kondeti, Phani Krishna
Ravi, Kumar
Mutheneni, Srinivasa Rao
Kadiri, Madhusudhan Rao
Kumaraswamy, Sriram
Vadlamani, Ravi
Upadhyayula, Suryanaryana Murty
author_facet Kondeti, Phani Krishna
Ravi, Kumar
Mutheneni, Srinivasa Rao
Kadiri, Madhusudhan Rao
Kumaraswamy, Sriram
Vadlamani, Ravi
Upadhyayula, Suryanaryana Murty
author_sort Kondeti, Phani Krishna
collection PubMed
description Filariasis is one of the major public health concerns in India. Approximately 600 million people spread across 250 districts of India are at risk of filariasis. To predict this disease, a pilot scale study was carried out in 30 villages of Karimnagar district of Telangana from 2004 to 2007 to collect epidemiological and socio-economic data. The collected data are analysed by employing various machine learning techniques such as Naïve Bayes (NB), logistic model tree, probabilistic neural network, J48 (C4.5), classification and regression tree, JRip and gradient boosting machine. The performances of these algorithms are reported using sensitivity, specificity, accuracy and area under ROC curve (AUC). Among all employed classification methods, NB yielded the best AUC of 64% and was equally statistically significant with the rest of the classifiers. Similarly, the J48 algorithm generated 23 decision rules that help in developing an early warning system to implement better prevention and control efforts in the management of filariasis.
format Online
Article
Text
id pubmed-6805759
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher Cambridge University Press
record_format MEDLINE/PubMed
spelling pubmed-68057592019-11-01 Applications of machine learning techniques to predict filariasis using socio-economic factors Kondeti, Phani Krishna Ravi, Kumar Mutheneni, Srinivasa Rao Kadiri, Madhusudhan Rao Kumaraswamy, Sriram Vadlamani, Ravi Upadhyayula, Suryanaryana Murty Epidemiol Infect Original Paper Filariasis is one of the major public health concerns in India. Approximately 600 million people spread across 250 districts of India are at risk of filariasis. To predict this disease, a pilot scale study was carried out in 30 villages of Karimnagar district of Telangana from 2004 to 2007 to collect epidemiological and socio-economic data. The collected data are analysed by employing various machine learning techniques such as Naïve Bayes (NB), logistic model tree, probabilistic neural network, J48 (C4.5), classification and regression tree, JRip and gradient boosting machine. The performances of these algorithms are reported using sensitivity, specificity, accuracy and area under ROC curve (AUC). Among all employed classification methods, NB yielded the best AUC of 64% and was equally statistically significant with the rest of the classifiers. Similarly, the J48 algorithm generated 23 decision rules that help in developing an early warning system to implement better prevention and control efforts in the management of filariasis. Cambridge University Press 2019-09-02 /pmc/articles/PMC6805759/ /pubmed/31475670 http://dx.doi.org/10.1017/S0950268819001481 Text en © The Author(s) 2019 http://creativecommons.org/licenses/by/4.0/ This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Paper
Kondeti, Phani Krishna
Ravi, Kumar
Mutheneni, Srinivasa Rao
Kadiri, Madhusudhan Rao
Kumaraswamy, Sriram
Vadlamani, Ravi
Upadhyayula, Suryanaryana Murty
Applications of machine learning techniques to predict filariasis using socio-economic factors
title Applications of machine learning techniques to predict filariasis using socio-economic factors
title_full Applications of machine learning techniques to predict filariasis using socio-economic factors
title_fullStr Applications of machine learning techniques to predict filariasis using socio-economic factors
title_full_unstemmed Applications of machine learning techniques to predict filariasis using socio-economic factors
title_short Applications of machine learning techniques to predict filariasis using socio-economic factors
title_sort applications of machine learning techniques to predict filariasis using socio-economic factors
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6805759/
https://www.ncbi.nlm.nih.gov/pubmed/31475670
http://dx.doi.org/10.1017/S0950268819001481
work_keys_str_mv AT kondetiphanikrishna applicationsofmachinelearningtechniquestopredictfilariasisusingsocioeconomicfactors
AT ravikumar applicationsofmachinelearningtechniquestopredictfilariasisusingsocioeconomicfactors
AT muthenenisrinivasarao applicationsofmachinelearningtechniquestopredictfilariasisusingsocioeconomicfactors
AT kadirimadhusudhanrao applicationsofmachinelearningtechniquestopredictfilariasisusingsocioeconomicfactors
AT kumaraswamysriram applicationsofmachinelearningtechniquestopredictfilariasisusingsocioeconomicfactors
AT vadlamaniravi applicationsofmachinelearningtechniquestopredictfilariasisusingsocioeconomicfactors
AT upadhyayulasuryanaryanamurty applicationsofmachinelearningtechniquestopredictfilariasisusingsocioeconomicfactors