Enhanced Neutrosophic Set and Machine Learning Approach for Breast Cancer Prediction
Breast cancer is the most prevalent type of cancer that affects women worldwide and poses a serious risk to female mortality. In order to lower death rates and enhance treatment results, early detection is critical. Neutrosophic Set Theory (NST) and machine learning (ML) approaches are integrated in...
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| Format: | Article |
| Language: | English |
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University of New Mexico
2024-11-01
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| Series: | Neutrosophic Sets and Systems |
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| Online Access: | https://fs.unm.edu/NSS/19Ashika_EnhancedNeutrosophic.pdf |
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| _version_ | 1849228897611153408 |
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| author | Ashika T Hannah Grace Nivetha Martin Florentin Smarandache |
| author_facet | Ashika T Hannah Grace Nivetha Martin Florentin Smarandache |
| author_sort | Ashika T |
| collection | DOAJ |
| description | Breast cancer is the most prevalent type of cancer that affects women worldwide and poses a serious risk to female mortality. In order to lower death rates and enhance treatment results, early detection is critical. Neutrosophic Set Theory (NST) and machine learning (ML) approaches are integrated in this study to provide a novel hybrid methodology (NS-ML) that improves breast cancer diagnosis. Using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, the research transforms these data into Neutrosophic (N) representations to effectively capture uncertainties. When trained on the N-dataset instead of traditional datasets, ML algorithms such as Decision Tree (DT), Random Forest (RF), and Adaptive Boosting (AdaBoost) perform better. Notably, N-AdaBoost models achieve outstanding results with 99.12% accuracy and 100% precision, highlighting the efficacy of NS in enhancing diagnostic reliability. |
| format | Article |
| id | doaj-art-c2ae35a6af0d4ec9bc03daf15ac12276 |
| institution | Kabale University |
| issn | 2331-6055 2331-608X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | University of New Mexico |
| record_format | Article |
| series | Neutrosophic Sets and Systems |
| spelling | doaj-art-c2ae35a6af0d4ec9bc03daf15ac122762025-08-22T12:34:28ZengUniversity of New MexicoNeutrosophic Sets and Systems2331-60552331-608X2024-11-017320621710.5281/zenodo.13989183Enhanced Neutrosophic Set and Machine Learning Approach for Breast Cancer PredictionAshika THannah GraceNivetha MartinFlorentin SmarandacheBreast cancer is the most prevalent type of cancer that affects women worldwide and poses a serious risk to female mortality. In order to lower death rates and enhance treatment results, early detection is critical. Neutrosophic Set Theory (NST) and machine learning (ML) approaches are integrated in this study to provide a novel hybrid methodology (NS-ML) that improves breast cancer diagnosis. Using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, the research transforms these data into Neutrosophic (N) representations to effectively capture uncertainties. When trained on the N-dataset instead of traditional datasets, ML algorithms such as Decision Tree (DT), Random Forest (RF), and Adaptive Boosting (AdaBoost) perform better. Notably, N-AdaBoost models achieve outstanding results with 99.12% accuracy and 100% precision, highlighting the efficacy of NS in enhancing diagnostic reliability.https://fs.unm.edu/NSS/19Ashika_EnhancedNeutrosophic.pdfneutrosophic setsmachine learninguncertainity handlingbreast cancerclassification |
| spellingShingle | Ashika T Hannah Grace Nivetha Martin Florentin Smarandache Enhanced Neutrosophic Set and Machine Learning Approach for Breast Cancer Prediction Neutrosophic Sets and Systems neutrosophic sets machine learning uncertainity handling breast cancer classification |
| title | Enhanced Neutrosophic Set and Machine Learning Approach for Breast Cancer Prediction |
| title_full | Enhanced Neutrosophic Set and Machine Learning Approach for Breast Cancer Prediction |
| title_fullStr | Enhanced Neutrosophic Set and Machine Learning Approach for Breast Cancer Prediction |
| title_full_unstemmed | Enhanced Neutrosophic Set and Machine Learning Approach for Breast Cancer Prediction |
| title_short | Enhanced Neutrosophic Set and Machine Learning Approach for Breast Cancer Prediction |
| title_sort | enhanced neutrosophic set and machine learning approach for breast cancer prediction |
| topic | neutrosophic sets machine learning uncertainity handling breast cancer classification |
| url | https://fs.unm.edu/NSS/19Ashika_EnhancedNeutrosophic.pdf |
| work_keys_str_mv | AT ashikat enhancedneutrosophicsetandmachinelearningapproachforbreastcancerprediction AT hannahgrace enhancedneutrosophicsetandmachinelearningapproachforbreastcancerprediction AT nivethamartin enhancedneutrosophicsetandmachinelearningapproachforbreastcancerprediction AT florentinsmarandache enhancedneutrosophicsetandmachinelearningapproachforbreastcancerprediction |