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....

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:International journal of information technology (Singapore. Online) Jg. 12; H. 3; S. 731 - 739
Hauptverfasser: Khanday, Akib Mohi Ud Din, Rabani, Syed Tanzeel, Khan, Qamar Rayees, Rouf, Nusrat, Mohi Ud Din, Masarat
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Singapore Springer Singapore 01.09.2020
Springer Nature B.V
Schlagworte:
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
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2511-2104
2511-2112
2511-2112
DOI:10.1007/s41870-020-00495-9