Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/15644
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dc.contributor.authorVanitha, K-
dc.contributor.authorKarpagavalli, S M-
dc.contributor.authorMahesh, T R-
dc.contributor.authorAli, A Althaf-
dc.contributor.authorSridhar, T-
dc.contributor.authorAnitha, K-
dc.date.accessioned2024-05-29T08:51:26Z-
dc.date.available2024-05-29T08:51:26Z-
dc.date.issued2024-
dc.identifier.citationpp. 1-6en_US
dc.identifier.isbn9798350360523-
dc.identifier.urihttp://dx.doi.org/10.1109/IATMSI60426.2024.10503434-
dc.identifier.urihttp://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/15644-
dc.description.abstractBreast Carcinoma, generally known as breast cancer, primarily affects women, though men can develop it as well. Because of the existence of breast tissue and exposure to female hormones, notably oestrogen, women are at a higher risk It's critical to diagnose breast tumors early. Several techniques based on machine learning (ML were used in this study to classify breast cancer using a dataset that was made available to the public. F-score, recall, precision, preciseness, and other performance metrics were used to evaluate these ML algorithms. Previous research and experimental findings indicate that Random Forest achieved the highest accuracy, with a remarkable accuracy rate of 99.12%. © 2024 IEEE.en_US
dc.language.isoenen_US
dc.publisherIEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024en_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.subjectBreast Canceren_US
dc.subjectKnnen_US
dc.subjectMachine Learningen_US
dc.subjectPerformanceen_US
dc.subjectSvmen_US
dc.titlePerformance Analysis of the Machine Learning Algorithms for the Early Detection of Breast Carcinomaen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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