A support vector machine for lung cancer detection with classification and compared with KNN for better accuracy

The primary objective of this research is to diagnose lung cancer more accurately utilizing the Support Vector Machine (SVM) method and K-Nearest Neighbors classification (KNN) technique. Materials and Methods - SVM was used in this study to detect lung cancer using 10 samples (N=10), while KNN was...

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Bibliographic Details
Main Authors: Rakesh, M., Baskar, Radhika
Format: Conference Proceeding
Language:English
Subjects:
Algorithms
Classification
Support vector machines
Online Access:Get full text
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Summary:The primary objective of this research is to diagnose lung cancer more accurately utilizing the Support Vector Machine (SVM) method and K-Nearest Neighbors classification (KNN) technique. Materials and Methods - SVM was used in this study to detect lung cancer using 10 samples (N=10), while KNN was used to classify the samples using 10 samples (N=10). The accuracy of the two approaches is contrasted. Results: The SVM method outperforms the KNN approach in accuracy (96% vs. 68%). Conclusion - It is clear from the results of the experimental testing that SVM performs better on lung imaging datasets than existing classifiers. The findings indicate that, for the identification of lung cancer, the SVM algorithm appears to be significantly better (p
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0198176