Dengue Fever Detection using Long Short-term Memory Neural Network

Dengue Fever Detection using Long Short-term Memory Neural Network

© 2020 IEEE. In this research, long short-term memory is used for text classification. The LSTM model is used for the detection of dengue fever from symptoms. The inputs of the model are the text of symptoms in Thai language, as well as the sex and age of the patients. For Thai text processing, firs...

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Main Authors: Wanchaloem Nadda, Waraporn Boonchieng, Ekkarat Boonchieng
Format: Conference Proceeding
Published: 2020
Subjects:
Computer Science
Decision Sciences
Engineering
Physics and Astronomy
Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85091835865&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/70427
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Institution: Chiang Mai University
id th-cmuir.6653943832-70427
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spelling th-cmuir.6653943832-704272020-10-14T08:49:37Z Dengue Fever Detection using Long Short-term Memory Neural Network Wanchaloem Nadda Waraporn Boonchieng Ekkarat Boonchieng Computer Science Decision Sciences Engineering Physics and Astronomy © 2020 IEEE. In this research, long short-term memory is used for text classification. The LSTM model is used for the detection of dengue fever from symptoms. The inputs of the model are the text of symptoms in Thai language, as well as the sex and age of the patients. For Thai text processing, first, we will token the sentence to words, and then correct the wrong words, and convert the words to vector using Word2Vec model and set as input data for LSTM model training. In addition, we use class balanced cross-entropy loss function for solving class imbalanced data problems. The results show that the G-mean (geometric mean of the accuracy of all classes) of LSTM with class balanced cross-entropy loss of function is greater than LSTM with cross-entropy loss function. 2020-10-14T08:30:16Z 2020-10-14T08:30:16Z 2020-06-01 Conference Proceeding 2-s2.0-85091835865 10.1109/ECTI-CON49241.2020.9158315 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85091835865&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/70427
institution Chiang Mai University
building Chiang Mai University Library
continent Asia
country Thailand
Thailand
content_provider Chiang Mai University Library
collection CMU Intellectual Repository
topic Computer Science
Decision Sciences
Engineering
Physics and Astronomy
spellingShingle Computer Science
Decision Sciences
Engineering
Physics and Astronomy
Wanchaloem Nadda
Waraporn Boonchieng
Ekkarat Boonchieng
Dengue Fever Detection using Long Short-term Memory Neural Network
description © 2020 IEEE. In this research, long short-term memory is used for text classification. The LSTM model is used for the detection of dengue fever from symptoms. The inputs of the model are the text of symptoms in Thai language, as well as the sex and age of the patients. For Thai text processing, first, we will token the sentence to words, and then correct the wrong words, and convert the words to vector using Word2Vec model and set as input data for LSTM model training. In addition, we use class balanced cross-entropy loss function for solving class imbalanced data problems. The results show that the G-mean (geometric mean of the accuracy of all classes) of LSTM with class balanced cross-entropy loss of function is greater than LSTM with cross-entropy loss function.
format Conference Proceeding
author Wanchaloem Nadda
Waraporn Boonchieng
Ekkarat Boonchieng
author_facet Wanchaloem Nadda
Waraporn Boonchieng
Ekkarat Boonchieng
author_sort Wanchaloem Nadda
title Dengue Fever Detection using Long Short-term Memory Neural Network
title_short Dengue Fever Detection using Long Short-term Memory Neural Network
title_full Dengue Fever Detection using Long Short-term Memory Neural Network
title_fullStr Dengue Fever Detection using Long Short-term Memory Neural Network
title_full_unstemmed Dengue Fever Detection using Long Short-term Memory Neural Network
title_sort dengue fever detection using long short-term memory neural network
publishDate 2020
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85091835865&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/70427
_version_ 1681752900705976320

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