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DC Field | Value | Language |
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dc.contributor.author | Vanitha, K | - |
dc.contributor.author | Karpagavalli, S M | - |
dc.contributor.author | Mahesh, T R | - |
dc.contributor.author | Ali, A Althaf | - |
dc.contributor.author | Sridhar, T | - |
dc.contributor.author | Anitha, K | - |
dc.date.accessioned | 2024-05-29T08:51:26Z | - |
dc.date.available | 2024-05-29T08:51:26Z | - |
dc.date.issued | 2024 | - |
dc.identifier.citation | pp. 1-6 | en_US |
dc.identifier.isbn | 9798350360523 | - |
dc.identifier.uri | http://dx.doi.org/10.1109/IATMSI60426.2024.10503434 | - |
dc.identifier.uri | http://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/15644 | - |
dc.description.abstract | Breast 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.iso | en | en_US |
dc.publisher | IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024 | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
dc.subject | Breast Cancer | en_US |
dc.subject | Knn | en_US |
dc.subject | Machine Learning | en_US |
dc.subject | Performance | en_US |
dc.subject | Svm | en_US |
dc.title | Performance Analysis of the Machine Learning Algorithms for the Early Detection of Breast Carcinoma | en_US |
dc.type | Article | en_US |
Appears in Collections: | Conference Papers |
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