Machine learning based approaches for detecting COVID-19 using clinical text data

Technology advancements have a rapid effect on every field of life, be it medical field or any other field. Artificial intelligence has shown the promising results in health care through its decision making by analysing the data. COVID-19 has affected more than 100 countries in a matter of no time....

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Vydáno v:	International journal of information technology (Singapore. Online) Ročník 12; číslo 3; s. 731 - 739
Hlavní autoři:	Khanday, Akib Mohi Ud Din, Rabani, Syed Tanzeel, Khan, Qamar Rayees, Rouf, Nusrat, Mohi Ud Din, Masarat
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Singapore Springer Singapore 01.09.2020 Springer Nature B.V
Témata:	Algorithms Artificial Intelligence Computer Imaging Computer Science Coronaviruses COVID-19 Decision analysis Decision making Engineering education Image Processing and Computer Vision Machine Learning Original Research Pattern Recognition and Graphics Recurrent neural networks Regression analysis Software Engineering Viral diseases Vision COVID-19 Artificial intelligence Machine learning Ensemble Imperative
ISSN:	2511-2104, 2511-2112, 2511-2112
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Abstract	Technology advancements have a rapid effect on every field of life, be it medical field or any other field. Artificial intelligence has shown the promising results in health care through its decision making by analysing the data. COVID-19 has affected more than 100 countries in a matter of no time. People all over the world are vulnerable to its consequences in future. It is imperative to develop a control system that will detect the coronavirus. One of the solution to control the current havoc can be the diagnosis of disease with the help of various AI tools. In this paper, we classified textual clinical reports into four classes by using classical and ensemble machine learning algorithms. Feature engineering was performed using techniques like Term frequency/inverse document frequency (TF/IDF), Bag of words (BOW) and report length. These features were supplied to traditional and ensemble machine learning classifiers. Logistic regression and Multinomial Naïve Bayes showed better results than other ML algorithms by having 96.2% testing accuracy. In future recurrent neural network can be used for better accuracy.
AbstractList	Technology advancements have a rapid effect on every field of life, be it medical field or any other field. Artificial intelligence has shown the promising results in health care through its decision making by analysing the data. COVID-19 has affected more than 100 countries in a matter of no time. People all over the world are vulnerable to its consequences in future. It is imperative to develop a control system that will detect the coronavirus. One of the solution to control the current havoc can be the diagnosis of disease with the help of various AI tools. In this paper, we classified textual clinical reports into four classes by using classical and ensemble machine learning algorithms. Feature engineering was performed using techniques like Term frequency/inverse document frequency (TF/IDF), Bag of words (BOW) and report length. These features were supplied to traditional and ensemble machine learning classifiers. Logistic regression and Multinomial Naïve Bayes showed better results than other ML algorithms by having 96.2% testing accuracy. In future recurrent neural network can be used for better accuracy. Technology advancements have a rapid effect on every field of life, be it medical field or any other field. Artificial intelligence has shown the promising results in health care through its decision making by analysing the data. COVID-19 has affected more than 100 countries in a matter of no time. People all over the world are vulnerable to its consequences in future. It is imperative to develop a control system that will detect the coronavirus. One of the solution to control the current havoc can be the diagnosis of disease with the help of various AI tools. In this paper, we classified textual clinical reports into four classes by using classical and ensemble machine learning algorithms. Feature engineering was performed using techniques like Term frequency/inverse document frequency (TF/IDF), Bag of words (BOW) and report length. These features were supplied to traditional and ensemble machine learning classifiers. Logistic regression and Multinomial Naïve Bayes showed better results than other ML algorithms by having 96.2% testing accuracy. In future recurrent neural network can be used for better accuracy.Technology advancements have a rapid effect on every field of life, be it medical field or any other field. Artificial intelligence has shown the promising results in health care through its decision making by analysing the data. COVID-19 has affected more than 100 countries in a matter of no time. People all over the world are vulnerable to its consequences in future. It is imperative to develop a control system that will detect the coronavirus. One of the solution to control the current havoc can be the diagnosis of disease with the help of various AI tools. In this paper, we classified textual clinical reports into four classes by using classical and ensemble machine learning algorithms. Feature engineering was performed using techniques like Term frequency/inverse document frequency (TF/IDF), Bag of words (BOW) and report length. These features were supplied to traditional and ensemble machine learning classifiers. Logistic regression and Multinomial Naïve Bayes showed better results than other ML algorithms by having 96.2% testing accuracy. In future recurrent neural network can be used for better accuracy.
Author	Khanday, Akib Mohi Ud Din Rouf, Nusrat Rabani, Syed Tanzeel Mohi Ud Din, Masarat Khan, Qamar Rayees
Author_xml	– sequence: 1 givenname: Akib Mohi Ud Din orcidid: 0000-0001-6804-4905 surname: Khanday fullname: Khanday, Akib Mohi Ud Din email: akibkhanday@bgsbu.ac.in organization: Department of Computer Sciences, Baba Ghulam Shah Badshah University – sequence: 2 givenname: Syed Tanzeel surname: Rabani fullname: Rabani, Syed Tanzeel organization: Department of Computer Sciences, Baba Ghulam Shah Badshah University – sequence: 3 givenname: Qamar Rayees surname: Khan fullname: Khan, Qamar Rayees organization: Department of Computer Sciences, Baba Ghulam Shah Badshah University – sequence: 4 givenname: Nusrat surname: Rouf fullname: Rouf, Nusrat organization: Department of Computer Sciences, Baba Ghulam Shah Badshah University – sequence: 5 givenname: Masarat surname: Mohi Ud Din fullname: Mohi Ud Din, Masarat organization: Government Medical College
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Snippet	Technology advancements have a rapid effect on every field of life, be it medical field or any other field. Artificial intelligence has shown the promising...
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SubjectTerms	Algorithms Artificial Intelligence Computer Imaging Computer Science Coronaviruses COVID-19 Decision analysis Decision making Engineering education Image Processing and Computer Vision Machine Learning Original Research Pattern Recognition and Graphics Recurrent neural networks Regression analysis Software Engineering Viral diseases Vision
Title	Machine learning based approaches for detecting COVID-19 using clinical text data
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