Classification of Benign and Malignant Breast Cancer using Supervised Machine Learning Algorithms Based on Image and Numeric Datasets Article Swipe
Ratula Ray
,
Azian Azamimi Abdullah
,
Debasish Kumar Mallick
,
Satya Ranjan Dash
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/1372/1/012062
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/1372/1/012062
Breast cancer has been identified as the second leading cause of death among women worldwide after lung cancer and hence, it becomes extremely crucial to identify it at an early stage, which can considerably increase the chances of survival. The most important part in cancer detection is to be able to differentiate between benign and malignant tumors and this is where the work of Machine Learning comes in. Taking all the dependent features upon consideration, Supervised Machine Learning methods allow for classification with higher degree of accuracy and improve upon the misdiagnosis of the physicians, which might occur almost 20% of the time. In our paper, we are focusing towards understanding the shortcomings of digital mammograms in detection of breast cancer and utilize Machine Learning classifiers for the classification of benign and malignant tumors using image analysis. Apart from this, we are also looking into implementing Supervised Machine Learning classifiers such as Decision Tree, K Nearest Neighbour (KNN), Random Forest and Gaussian Naive Bayes classifiers for assessing the risks involved with breast cancer by analyzing the biomarkers that are involved with it. Our aim is to provide a comprehensive view on prediction of breast cancer through Machine Learning through both image and data analyses, which can play a pivotal role in prevention of misdiagnosis in future. Fig. 1. gives a layout for the breast cancer prediction using Supervised Machine learning classifiers.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1742-6596/1372/1/012062
- OA Status
- diamond
- Cited By
- 18
- References
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- OpenAlex ID
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2989580458Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1088/1742-6596/1372/1/012062Digital Object Identifier
- Title
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Classification of Benign and Malignant Breast Cancer using Supervised Machine Learning Algorithms Based on Image and Numeric DatasetsWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2019Year of publication
- Publication date
-
2019-11-01Full publication date if available
- Authors
-
Ratula Ray, Azian Azamimi Abdullah, Debasish Kumar Mallick, Satya Ranjan DashList of authors in order
- Landing page
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https://doi.org/10.1088/1742-6596/1372/1/012062Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
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https://doi.org/10.1088/1742-6596/1372/1/012062Direct OA link when available
- Concepts
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Machine learning, Artificial intelligence, Decision tree, Random forest, Naive Bayes classifier, Computer science, Breast cancer, Statistical classification, Cancer, Decision tree learning, Support vector machine, Medicine, Internal medicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
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18Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 3, 2023: 4, 2022: 4, 2021: 6Per-year citation counts (last 5 years)
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7Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| corresponding_author_ids | https://openalex.org/A5076382765 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I67357951 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.9100000262260437 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.8699296 |
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| citation_normalized_percentile.is_in_top_10_percent | False |