Response and Surveillance System for Diarrhoea Based on a Patient Symptoms Using Machine Learning: A Study on Eswatini

Utilizing supervised machine learning algorithms to develop a surveillance and response system based on symptoms of diarrhoea, contingent on the Support Vector Machine (SVM) to predict the probable disease using labelled data. Diarrhoea is amongst the top ten diseases which kill. A prototype system...

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Autores principales:	Sibongakonke Kwanele Zungu, Qi-Xian Huang, Min-Yi Chiu, Hung-Min Sun
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Diarrhoea prognosis supervised machine learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/1d64f687002545eb9eb015da74a8a6e3
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spelling	oai:doaj.org-article:1d64f687002545eb9eb015da74a8a6e32021-11-26T00:00:19ZResponse and Surveillance System for Diarrhoea Based on a Patient Symptoms Using Machine Learning: A Study on Eswatini2169-353610.1109/ACCESS.2021.3124964https://doaj.org/article/1d64f687002545eb9eb015da74a8a6e32021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9598837/https://doaj.org/toc/2169-3536Utilizing supervised machine learning algorithms to develop a surveillance and response system based on symptoms of diarrhoea, contingent on the Support Vector Machine (SVM) to predict the probable disease using labelled data. Diarrhoea is amongst the top ten diseases which kill. A prototype system is developed based on the SVM algorithm. The prototype system takes six patient symptoms that which is input, from the user and the output result becomes the prognosis which may likely occur based solely on the given symptoms. Two other supervised learning models have been utilized in the prediction process, Random Forest Model (RFC) and Naïve Bayes Model (NB). Furthermore, a visualization on google maps (my maps) on the area in which a diarrhoea outbreak would likely occur. The constituency and the region of the patient will be used to place a pin on my maps, giving a visualization on the map, with a mapping structure this allows for a vivid demonstration of how diarrhoea is spreading in Eswatini. SVM received an average of 100% accuracy. The other two supervised learning models, random forest model and naïve Bayes model received 97.62% average accuracy on the same dataset. It shows that the SVM does well in data classification and with a small dataset.Sibongakonke Kwanele ZunguQi-Xian HuangMin-Yi ChiuHung-Min SunIEEEarticleDiarrhoeaprognosissupervised machine learningElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 152945-152959 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Diarrhoea prognosis supervised machine learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Diarrhoea prognosis supervised machine learning Electrical engineering. Electronics. Nuclear engineering TK1-9971 Sibongakonke Kwanele Zungu Qi-Xian Huang Min-Yi Chiu Hung-Min Sun Response and Surveillance System for Diarrhoea Based on a Patient Symptoms Using Machine Learning: A Study on Eswatini
description	Utilizing supervised machine learning algorithms to develop a surveillance and response system based on symptoms of diarrhoea, contingent on the Support Vector Machine (SVM) to predict the probable disease using labelled data. Diarrhoea is amongst the top ten diseases which kill. A prototype system is developed based on the SVM algorithm. The prototype system takes six patient symptoms that which is input, from the user and the output result becomes the prognosis which may likely occur based solely on the given symptoms. Two other supervised learning models have been utilized in the prediction process, Random Forest Model (RFC) and Naïve Bayes Model (NB). Furthermore, a visualization on google maps (my maps) on the area in which a diarrhoea outbreak would likely occur. The constituency and the region of the patient will be used to place a pin on my maps, giving a visualization on the map, with a mapping structure this allows for a vivid demonstration of how diarrhoea is spreading in Eswatini. SVM received an average of 100% accuracy. The other two supervised learning models, random forest model and naïve Bayes model received 97.62% average accuracy on the same dataset. It shows that the SVM does well in data classification and with a small dataset.
format	article
author	Sibongakonke Kwanele Zungu Qi-Xian Huang Min-Yi Chiu Hung-Min Sun
author_facet	Sibongakonke Kwanele Zungu Qi-Xian Huang Min-Yi Chiu Hung-Min Sun
author_sort	Sibongakonke Kwanele Zungu
title	Response and Surveillance System for Diarrhoea Based on a Patient Symptoms Using Machine Learning: A Study on Eswatini
title_short	Response and Surveillance System for Diarrhoea Based on a Patient Symptoms Using Machine Learning: A Study on Eswatini
title_full	Response and Surveillance System for Diarrhoea Based on a Patient Symptoms Using Machine Learning: A Study on Eswatini
title_fullStr	Response and Surveillance System for Diarrhoea Based on a Patient Symptoms Using Machine Learning: A Study on Eswatini
title_full_unstemmed	Response and Surveillance System for Diarrhoea Based on a Patient Symptoms Using Machine Learning: A Study on Eswatini
title_sort	response and surveillance system for diarrhoea based on a patient symptoms using machine learning: a study on eswatini
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/1d64f687002545eb9eb015da74a8a6e3
work_keys_str_mv	AT sibongakonkekwanelezungu responseandsurveillancesystemfordiarrhoeabasedonapatientsymptomsusingmachinelearningastudyoneswatini AT qixianhuang responseandsurveillancesystemfordiarrhoeabasedonapatientsymptomsusingmachinelearningastudyoneswatini AT minyichiu responseandsurveillancesystemfordiarrhoeabasedonapatientsymptomsusingmachinelearningastudyoneswatini AT hungminsun responseandsurveillancesystemfordiarrhoeabasedonapatientsymptomsusingmachinelearningastudyoneswatini
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Response and Surveillance System for Diarrhoea Based on a Patient Symptoms Using Machine Learning: A Study on Eswatini

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