Prediction of dengue outbreak in Selangor Malaysia using machine learning techniques

Abstract Dengue fever is a mosquito-borne disease that affects nearly 3.9 billion people globally. Dengue remains endemic in Malaysia since its outbreak in the 1980’s, with its highest concentration of cases in the state of Selangor. Predictors of dengue fever outbreaks could provide timely informat...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Nurul Azam Mohd Salim, Yap Bee Wah, Caitlynn Reeves, Madison Smith, Wan Fairos Wan Yaacob, Rose Nani Mudin, Rahmat Dapari, Nik Nur Fatin Fatihah Sapri, Ubydul Haque
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6cc4bab490ab45e8b72e132c9c65f8ee
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:6cc4bab490ab45e8b72e132c9c65f8ee
record_format	dspace
spelling	oai:doaj.org-article:6cc4bab490ab45e8b72e132c9c65f8ee2021-12-02T15:22:56ZPrediction of dengue outbreak in Selangor Malaysia using machine learning techniques10.1038/s41598-020-79193-22045-2322https://doaj.org/article/6cc4bab490ab45e8b72e132c9c65f8ee2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79193-2https://doaj.org/toc/2045-2322Abstract Dengue fever is a mosquito-borne disease that affects nearly 3.9 billion people globally. Dengue remains endemic in Malaysia since its outbreak in the 1980’s, with its highest concentration of cases in the state of Selangor. Predictors of dengue fever outbreaks could provide timely information for health officials to implement preventative actions. In this study, five districts in Selangor, Malaysia, that demonstrated the highest incidence of dengue fever from 2013 to 2017 were evaluated for the best machine learning model to predict Dengue outbreaks. Climate variables such as temperature, wind speed, humidity and rainfall were used in each model. Based on results, the SVM (linear kernel) exhibited the best prediction performance (Accuracy = 70%, Sensitivity = 14%, Specificity = 95%, Precision = 56%). However, the sensitivity for SVM (linear) for the testing sample increased up to 63.54% compared to 14.4% for imbalanced data (original data). The week-of-the-year was the most important predictor in the SVM model. This study exemplifies that machine learning has respectable potential for the prediction of dengue outbreaks. Future research should consider boosting, or using, nature inspired algorithms to develop a dengue prediction model.Nurul Azam Mohd SalimYap Bee WahCaitlynn ReevesMadison SmithWan Fairos Wan YaacobRose Nani MudinRahmat DapariNik Nur Fatin Fatihah SapriUbydul HaqueNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Nurul Azam Mohd Salim Yap Bee Wah Caitlynn Reeves Madison Smith Wan Fairos Wan Yaacob Rose Nani Mudin Rahmat Dapari Nik Nur Fatin Fatihah Sapri Ubydul Haque Prediction of dengue outbreak in Selangor Malaysia using machine learning techniques
description	Abstract Dengue fever is a mosquito-borne disease that affects nearly 3.9 billion people globally. Dengue remains endemic in Malaysia since its outbreak in the 1980’s, with its highest concentration of cases in the state of Selangor. Predictors of dengue fever outbreaks could provide timely information for health officials to implement preventative actions. In this study, five districts in Selangor, Malaysia, that demonstrated the highest incidence of dengue fever from 2013 to 2017 were evaluated for the best machine learning model to predict Dengue outbreaks. Climate variables such as temperature, wind speed, humidity and rainfall were used in each model. Based on results, the SVM (linear kernel) exhibited the best prediction performance (Accuracy = 70%, Sensitivity = 14%, Specificity = 95%, Precision = 56%). However, the sensitivity for SVM (linear) for the testing sample increased up to 63.54% compared to 14.4% for imbalanced data (original data). The week-of-the-year was the most important predictor in the SVM model. This study exemplifies that machine learning has respectable potential for the prediction of dengue outbreaks. Future research should consider boosting, or using, nature inspired algorithms to develop a dengue prediction model.
format	article
author	Nurul Azam Mohd Salim Yap Bee Wah Caitlynn Reeves Madison Smith Wan Fairos Wan Yaacob Rose Nani Mudin Rahmat Dapari Nik Nur Fatin Fatihah Sapri Ubydul Haque
author_facet	Nurul Azam Mohd Salim Yap Bee Wah Caitlynn Reeves Madison Smith Wan Fairos Wan Yaacob Rose Nani Mudin Rahmat Dapari Nik Nur Fatin Fatihah Sapri Ubydul Haque
author_sort	Nurul Azam Mohd Salim
title	Prediction of dengue outbreak in Selangor Malaysia using machine learning techniques
title_short	Prediction of dengue outbreak in Selangor Malaysia using machine learning techniques
title_full	Prediction of dengue outbreak in Selangor Malaysia using machine learning techniques
title_fullStr	Prediction of dengue outbreak in Selangor Malaysia using machine learning techniques
title_full_unstemmed	Prediction of dengue outbreak in Selangor Malaysia using machine learning techniques
title_sort	prediction of dengue outbreak in selangor malaysia using machine learning techniques
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/6cc4bab490ab45e8b72e132c9c65f8ee
work_keys_str_mv	AT nurulazammohdsalim predictionofdengueoutbreakinselangormalaysiausingmachinelearningtechniques AT yapbeewah predictionofdengueoutbreakinselangormalaysiausingmachinelearningtechniques AT caitlynnreeves predictionofdengueoutbreakinselangormalaysiausingmachinelearningtechniques AT madisonsmith predictionofdengueoutbreakinselangormalaysiausingmachinelearningtechniques AT wanfairoswanyaacob predictionofdengueoutbreakinselangormalaysiausingmachinelearningtechniques AT rosenanimudin predictionofdengueoutbreakinselangormalaysiausingmachinelearningtechniques AT rahmatdapari predictionofdengueoutbreakinselangormalaysiausingmachinelearningtechniques AT niknurfatinfatihahsapri predictionofdengueoutbreakinselangormalaysiausingmachinelearningtechniques AT ubydulhaque predictionofdengueoutbreakinselangormalaysiausingmachinelearningtechniques
_version_	1718387385914359808

Prediction of dengue outbreak in Selangor Malaysia using machine learning techniques

Ejemplares similares