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COVID-19 Detection in the Images of Chest CT Scan Using K-NN in Comparison with NB Classifier to Improve the Accuracy
1st International Conference on Advanced Communication and Intelligent Systems, ICACIS 2022 ; 1749 CCIS:756-763, 2023.
Article in English | Scopus | ID: covidwho-2261118
ABSTRACT
This chapter is about the improvisation in the accuracy in COVID-19 detection using chest CT-scan images through K-Nearest Neighbour (K-NN) compared with Naive-Bayes (NB) classifier. The sample size considered for this detection is 20, for group 1 and 2, where G-power is 0.8. The value of alpha and beta was 0.05 and 0.2 along with a confidence interval at 95%. The K-NN classifier has achieved 95.297% of higher accuracy rate when compared with Naive Bayes classifier 92.087%. The results obtained were considered to be error-free since it was having the significance value of 0.036 (p < 0.05). Therefore, in this work K-Nearest Neighbor has performed significantly better than Naive Bayes algorithm in detection of COVID-19. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 1st International Conference on Advanced Communication and Intelligent Systems, ICACIS 2022 Year: 2023 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 1st International Conference on Advanced Communication and Intelligent Systems, ICACIS 2022 Year: 2023 Document Type: Article