Comparative analysis of identifying accuracy of online misinformation of COVID-19 using SVM algorithm with decision tree classification
Alinteri Journal of Agriculture Science
; 36(1):507-512, 2021.
Article
in English
| GIM | ID: covidwho-1965183
ABSTRACT
Aim:
To improve the accuracy percentage of predicting misinformation about COVID-19 using SVM algorithm. Materials andmethods:
Support Vector Machine (SVM) with sample size = 20 and Decision Tree classification with sample size = 20 was iterated at different times for predicting the accuracy percentage of misinformation about COVID19. The Novel Poly kernel function used in SVM maps the dataset into higher dimensional space which helps to improve accuracy percentage. Results andDiscussion:
SVM has significantly better accuracy (94.48%) compared to Decision Tree accuracy (93%). There was a statistical significance between SVM and the Decision Tree (p=0.000) (p < 0.05 Independent Sample T-test).
accuracy; algorithms; datasets; sample size; samples; on line; information services; viral diseases; coronavirus disease 2019; human diseases; prediction; Severe acute respiratory syndrome coronavirus 2; man; Severe acute respiratory syndrome-related coronavirus; Betacoronavirus; Coronavirinae; Coronaviridae; Nidovirales; positive-sense ssRNA Viruses; ssRNA Viruses; RNA Viruses; viruses; Homo; Hominidae; primates; mammals; vertebrates; Chordata; animals; eukaryotes; information sources; SARS-CoV-2; viral infections
Full text:
Available
Collection:
Databases of international organizations
Database:
GIM
Language:
English
Journal:
Alinteri Journal of Agriculture Science
Year:
2021
Document Type:
Article
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