Breast cancer detection: an effective comparison of different machine learning algorithms on the Wisconsin dataset Article Swipe
Md. Murad Hossin
,
F. M. Javed Mehedi Shamrat
,
Md Rifat Bhuiyan
,
Rabea Akter Hira
,
Tamim Khan
,
Shourav Molla
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.11591/eei.v12i4.4448
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.11591/eei.v12i4.4448
According to the American cancer society, breast cancer is one of the leading causes of women's mortality worldwide. Early identification and treatment are the most effective approaches to halt the spread of this cancer. The objective of this article is to give a comparison of eight machine learning algorithms, including logistic regression (LR), random forest (RF), K-nearest neighbors (KNN), decision tree (DT), ada boost (AB), support vector machine (SVM), gradient boosting (GB), and Gaussian Naive Bayes (GNB) for breast cancer detection. The breast cancer Wisconsin (diagnostic) dataset is being utilized to validate the findings of this study. The comparison was made using the following performance metrics: accuracy, sensitivity, false omission rate, specificity, false discovery rate and area under curve. The LR method achieved a maximum accuracy of 99.12% among all eight algorithms and was compared to other comparable studies in the literature. The five features chosen are used to calculate the model's fidelity-to-interpretability ratio (FIR), which indicates how much interpretability was sacrificed for performance. The uniqueness of this work is the explainability approach taken in the model's performance, which aims to make the model's outputs more understandable and interpretable to healthcare experts.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.11591/eei.v12i4.4448
- OA Status
- diamond
- Cited By
- 21
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4327726800
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4327726800Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.11591/eei.v12i4.4448Digital Object Identifier
- Title
-
Breast cancer detection: an effective comparison of different machine learning algorithms on the Wisconsin datasetWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-03-17Full publication date if available
- Authors
-
Md. Murad Hossin, F. M. Javed Mehedi Shamrat, Md Rifat Bhuiyan, Rabea Akter Hira, Tamim Khan, Shourav MollaList of authors in order
- Landing page
-
https://doi.org/10.11591/eei.v12i4.4448Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.11591/eei.v12i4.4448Direct OA link when available
- Concepts
-
Interpretability, Machine learning, Random forest, Decision tree, Artificial intelligence, Naive Bayes classifier, Support vector machine, Breast cancer, Algorithm, Gradient boosting, Boosting (machine learning), Computer science, Logistic regression, Cancer, Medicine, Internal medicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
21Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5, 2024: 12, 2023: 4Per-year citation counts (last 5 years)
- References (count)
-
27Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4327726800 |
|---|---|
| doi | https://doi.org/10.11591/eei.v12i4.4448 |
| ids.doi | https://doi.org/10.11591/eei.v12i4.4448 |
| ids.openalex | https://openalex.org/W4327726800 |
| fwci | 5.36430064 |
| type | article |
| title | Breast cancer detection: an effective comparison of different machine learning algorithms on the Wisconsin dataset |
| biblio.issue | 4 |
| biblio.volume | 12 |
| biblio.last_page | 2456 |
| biblio.first_page | 2446 |
| topics[0].id | https://openalex.org/T10862 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9883000254631042 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | AI in cancer detection |
| topics[1].id | https://openalex.org/T10885 |
| topics[1].field.id | https://openalex.org/fields/13 |
| topics[1].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[1].score | 0.9093000292778015 |
| topics[1].domain.id | https://openalex.org/domains/1 |
| topics[1].domain.display_name | Life Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1312 |
| topics[1].subfield.display_name | Molecular Biology |
| topics[1].display_name | Gene expression and cancer classification |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C2781067378 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8495768308639526 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q17027399 |
| concepts[0].display_name | Interpretability |
| concepts[1].id | https://openalex.org/C119857082 |
| concepts[1].level | 1 |
| concepts[1].score | 0.7334177494049072 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[1].display_name | Machine learning |
| concepts[2].id | https://openalex.org/C169258074 |
| concepts[2].level | 2 |
| concepts[2].score | 0.7039669752120972 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q245748 |
| concepts[2].display_name | Random forest |
| concepts[3].id | https://openalex.org/C84525736 |
| concepts[3].level | 2 |
| concepts[3].score | 0.6390194296836853 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q831366 |
| concepts[3].display_name | Decision tree |
| concepts[4].id | https://openalex.org/C154945302 |
| concepts[4].level | 1 |
| concepts[4].score | 0.6297439336776733 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[4].display_name | Artificial intelligence |
| concepts[5].id | https://openalex.org/C52001869 |
| concepts[5].level | 3 |
| concepts[5].score | 0.6188186407089233 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q812530 |
| concepts[5].display_name | Naive Bayes classifier |
| concepts[6].id | https://openalex.org/C12267149 |
| concepts[6].level | 2 |
| concepts[6].score | 0.6089543104171753 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q282453 |
| concepts[6].display_name | Support vector machine |
| concepts[7].id | https://openalex.org/C530470458 |
| concepts[7].level | 3 |
| concepts[7].score | 0.587274968624115 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q128581 |
| concepts[7].display_name | Breast cancer |
| concepts[8].id | https://openalex.org/C11413529 |
| concepts[8].level | 1 |
| concepts[8].score | 0.5862792730331421 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[8].display_name | Algorithm |
| concepts[9].id | https://openalex.org/C70153297 |
| concepts[9].level | 3 |
| concepts[9].score | 0.5467055439949036 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q5591907 |
| concepts[9].display_name | Gradient boosting |
| concepts[10].id | https://openalex.org/C46686674 |
| concepts[10].level | 2 |
| concepts[10].score | 0.53664231300354 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q466303 |
| concepts[10].display_name | Boosting (machine learning) |
| concepts[11].id | https://openalex.org/C41008148 |
| concepts[11].level | 0 |
| concepts[11].score | 0.48925328254699707 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[11].display_name | Computer science |
| concepts[12].id | https://openalex.org/C151956035 |
| concepts[12].level | 2 |
| concepts[12].score | 0.4832380712032318 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q1132755 |
| concepts[12].display_name | Logistic regression |
| concepts[13].id | https://openalex.org/C121608353 |
| concepts[13].level | 2 |
| concepts[13].score | 0.3674026131629944 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q12078 |
| concepts[13].display_name | Cancer |
| concepts[14].id | https://openalex.org/C71924100 |
| concepts[14].level | 0 |
| concepts[14].score | 0.2286873161792755 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[14].display_name | Medicine |
| concepts[15].id | https://openalex.org/C126322002 |
| concepts[15].level | 1 |
| concepts[15].score | 0.10471999645233154 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q11180 |
| concepts[15].display_name | Internal medicine |
| keywords[0].id | https://openalex.org/keywords/interpretability |
| keywords[0].score | 0.8495768308639526 |
| keywords[0].display_name | Interpretability |
| keywords[1].id | https://openalex.org/keywords/machine-learning |
| keywords[1].score | 0.7334177494049072 |
| keywords[1].display_name | Machine learning |
| keywords[2].id | https://openalex.org/keywords/random-forest |
| keywords[2].score | 0.7039669752120972 |
| keywords[2].display_name | Random forest |
| keywords[3].id | https://openalex.org/keywords/decision-tree |
| keywords[3].score | 0.6390194296836853 |
| keywords[3].display_name | Decision tree |
| keywords[4].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[4].score | 0.6297439336776733 |
| keywords[4].display_name | Artificial intelligence |
| keywords[5].id | https://openalex.org/keywords/naive-bayes-classifier |
| keywords[5].score | 0.6188186407089233 |
| keywords[5].display_name | Naive Bayes classifier |
| keywords[6].id | https://openalex.org/keywords/support-vector-machine |
| keywords[6].score | 0.6089543104171753 |
| keywords[6].display_name | Support vector machine |
| keywords[7].id | https://openalex.org/keywords/breast-cancer |
| keywords[7].score | 0.587274968624115 |
| keywords[7].display_name | Breast cancer |
| keywords[8].id | https://openalex.org/keywords/algorithm |
| keywords[8].score | 0.5862792730331421 |
| keywords[8].display_name | Algorithm |
| keywords[9].id | https://openalex.org/keywords/gradient-boosting |
| keywords[9].score | 0.5467055439949036 |
| keywords[9].display_name | Gradient boosting |
| keywords[10].id | https://openalex.org/keywords/boosting |
| keywords[10].score | 0.53664231300354 |
| keywords[10].display_name | Boosting (machine learning) |
| keywords[11].id | https://openalex.org/keywords/computer-science |
| keywords[11].score | 0.48925328254699707 |
| keywords[11].display_name | Computer science |
| keywords[12].id | https://openalex.org/keywords/logistic-regression |
| keywords[12].score | 0.4832380712032318 |
| keywords[12].display_name | Logistic regression |
| keywords[13].id | https://openalex.org/keywords/cancer |
| keywords[13].score | 0.3674026131629944 |
| keywords[13].display_name | Cancer |
| keywords[14].id | https://openalex.org/keywords/medicine |
| keywords[14].score | 0.2286873161792755 |
| keywords[14].display_name | Medicine |
| keywords[15].id | https://openalex.org/keywords/internal-medicine |
| keywords[15].score | 0.10471999645233154 |
| keywords[15].display_name | Internal medicine |
| language | en |
| locations[0].id | doi:10.11591/eei.v12i4.4448 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S2765016208 |
| locations[0].source.issn | 2302-9285 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2302-9285 |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Bulletin of Electrical Engineering and Informatics |
| locations[0].source.host_organization | https://openalex.org/P4310315009 |
| locations[0].source.host_organization_name | Institute of Advanced Engineering and Science (IAES) |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310315009 |
| locations[0].source.host_organization_lineage_names | Institute of Advanced Engineering and Science (IAES) |
| locations[0].license | cc-by-sa |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by-sa |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Bulletin of Electrical Engineering and Informatics |
| locations[0].landing_page_url | https://doi.org/10.11591/eei.v12i4.4448 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5022975936 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-9918-8987 |
| authorships[0].author.display_name | Md. Murad Hossin |
| authorships[0].countries | BD |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I200606013 |
| authorships[0].affiliations[0].raw_affiliation_string | Daffodil International University |
| authorships[0].institutions[0].id | https://openalex.org/I200606013 |
| authorships[0].institutions[0].ror | https://ror.org/052t4a858 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I200606013 |
| authorships[0].institutions[0].country_code | BD |
| authorships[0].institutions[0].display_name | Daffodil International University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Md. Murad Hossin |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Daffodil International University |
| authorships[1].author.id | https://openalex.org/A5045615699 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-9176-3537 |
| authorships[1].author.display_name | F. M. Javed Mehedi Shamrat |
| authorships[1].countries | BD |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I200606013 |
| authorships[1].affiliations[0].raw_affiliation_string | Daffodil International University |
| authorships[1].institutions[0].id | https://openalex.org/I200606013 |
| authorships[1].institutions[0].ror | https://ror.org/052t4a858 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I200606013 |
| authorships[1].institutions[0].country_code | BD |
| authorships[1].institutions[0].display_name | Daffodil International University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | F. M. Javed Mehedi Shamrat |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Daffodil International University |
| authorships[2].author.id | https://openalex.org/A5072988203 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Md Rifat Bhuiyan |
| authorships[2].countries | BD |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I200606013 |
| authorships[2].affiliations[0].raw_affiliation_string | Daffodil International University |
| authorships[2].institutions[0].id | https://openalex.org/I200606013 |
| authorships[2].institutions[0].ror | https://ror.org/052t4a858 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I200606013 |
| authorships[2].institutions[0].country_code | BD |
| authorships[2].institutions[0].display_name | Daffodil International University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Md Rifat Bhuiyan |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Daffodil International University |
| authorships[3].author.id | https://openalex.org/A5009522247 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-4737-8361 |
| authorships[3].author.display_name | Rabea Akter Hira |
| authorships[3].countries | BD |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I4210132231 |
| authorships[3].affiliations[0].raw_affiliation_string | International University of Business Agriculture and Technology |
| authorships[3].institutions[0].id | https://openalex.org/I4210132231 |
| authorships[3].institutions[0].ror | https://ror.org/02m32cr13 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I4210132231 |
| authorships[3].institutions[0].country_code | BD |
| authorships[3].institutions[0].display_name | International University of Business Agriculture and Technology |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Rabea Akter Hira |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | International University of Business Agriculture and Technology |
| authorships[4].author.id | https://openalex.org/A5082407634 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-7850-0211 |
| authorships[4].author.display_name | Tamim Khan |
| authorships[4].countries | BD |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I200606013 |
| authorships[4].affiliations[0].raw_affiliation_string | Daffodil International University |
| authorships[4].institutions[0].id | https://openalex.org/I200606013 |
| authorships[4].institutions[0].ror | https://ror.org/052t4a858 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I200606013 |
| authorships[4].institutions[0].country_code | BD |
| authorships[4].institutions[0].display_name | Daffodil International University |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Tamim Khan |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Daffodil International University |
| authorships[5].author.id | https://openalex.org/A5005381636 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-8496-9972 |
| authorships[5].author.display_name | Shourav Molla |
| authorships[5].countries | BD |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I200606013 |
| authorships[5].affiliations[0].raw_affiliation_string | Daffodil International University |
| authorships[5].institutions[0].id | https://openalex.org/I200606013 |
| authorships[5].institutions[0].ror | https://ror.org/052t4a858 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I200606013 |
| authorships[5].institutions[0].country_code | BD |
| authorships[5].institutions[0].display_name | Daffodil International University |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Shourav Molla |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Daffodil International University |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.11591/eei.v12i4.4448 |
| open_access.oa_status | diamond |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Breast cancer detection: an effective comparison of different machine learning algorithms on the Wisconsin dataset |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10862 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9883000254631042 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | AI in cancer detection |
| related_works | https://openalex.org/W4281846282, https://openalex.org/W3204641204, https://openalex.org/W4293069612, https://openalex.org/W4308191010, https://openalex.org/W4200057378, https://openalex.org/W4375930479, https://openalex.org/W4214951795, https://openalex.org/W4327531700, https://openalex.org/W4327726800, https://openalex.org/W4328133444 |
| cited_by_count | 21 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 5 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 12 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 4 |
| locations_count | 1 |
| best_oa_location.id | doi:10.11591/eei.v12i4.4448 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2765016208 |
| best_oa_location.source.issn | 2302-9285 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2302-9285 |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Bulletin of Electrical Engineering and Informatics |
| best_oa_location.source.host_organization | https://openalex.org/P4310315009 |
| best_oa_location.source.host_organization_name | Institute of Advanced Engineering and Science (IAES) |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310315009 |
| best_oa_location.source.host_organization_lineage_names | Institute of Advanced Engineering and Science (IAES) |
| best_oa_location.license | cc-by-sa |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by-sa |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Bulletin of Electrical Engineering and Informatics |
| best_oa_location.landing_page_url | https://doi.org/10.11591/eei.v12i4.4448 |
| primary_location.id | doi:10.11591/eei.v12i4.4448 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S2765016208 |
| primary_location.source.issn | 2302-9285 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2302-9285 |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Bulletin of Electrical Engineering and Informatics |
| primary_location.source.host_organization | https://openalex.org/P4310315009 |
| primary_location.source.host_organization_name | Institute of Advanced Engineering and Science (IAES) |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310315009 |
| primary_location.source.host_organization_lineage_names | Institute of Advanced Engineering and Science (IAES) |
| primary_location.license | cc-by-sa |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by-sa |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Bulletin of Electrical Engineering and Informatics |
| primary_location.landing_page_url | https://doi.org/10.11591/eei.v12i4.4448 |
| publication_date | 2023-03-17 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W2579213716, https://openalex.org/W3197078391, https://openalex.org/W2966483024, https://openalex.org/W3092338125, https://openalex.org/W4299298161, https://openalex.org/W6675354045, https://openalex.org/W4225964155, https://openalex.org/W4226293401, https://openalex.org/W6811678227, https://openalex.org/W4322574962, https://openalex.org/W3172921504, https://openalex.org/W2951891094, https://openalex.org/W2085281262, https://openalex.org/W4240268957, https://openalex.org/W3148181069, https://openalex.org/W3120027565, https://openalex.org/W1985712316, https://openalex.org/W2768149277, https://openalex.org/W3210526060, https://openalex.org/W2996283780, https://openalex.org/W2216946510, https://openalex.org/W4381304672, https://openalex.org/W3121216533, https://openalex.org/W3081125651, https://openalex.org/W2101234009, https://openalex.org/W4244895750, https://openalex.org/W2787667267 |
| referenced_works_count | 27 |
| abstract_inverted_index.a | 42, 123 |
| abstract_inverted_index.LR | 120 |
| abstract_inverted_index.in | 139, 174 |
| abstract_inverted_index.is | 8, 39, 87, 169 |
| abstract_inverted_index.of | 10, 14, 31, 36, 44, 94, 126, 166 |
| abstract_inverted_index.to | 1, 27, 40, 90, 135, 148, 180, 189 |
| abstract_inverted_index.The | 34, 81, 97, 119, 142, 164 |
| abstract_inverted_index.ada | 62 |
| abstract_inverted_index.all | 129 |
| abstract_inverted_index.and | 20, 72, 115, 132, 187 |
| abstract_inverted_index.are | 22, 146 |
| abstract_inverted_index.for | 77, 162 |
| abstract_inverted_index.how | 157 |
| abstract_inverted_index.one | 9 |
| abstract_inverted_index.the | 2, 11, 23, 29, 92, 102, 140, 150, 170, 175, 182 |
| abstract_inverted_index.was | 99, 133, 160 |
| abstract_inverted_index.aims | 179 |
| abstract_inverted_index.area | 116 |
| abstract_inverted_index.five | 143 |
| abstract_inverted_index.give | 41 |
| abstract_inverted_index.halt | 28 |
| abstract_inverted_index.made | 100 |
| abstract_inverted_index.make | 181 |
| abstract_inverted_index.more | 185 |
| abstract_inverted_index.most | 24 |
| abstract_inverted_index.much | 158 |
| abstract_inverted_index.rate | 114 |
| abstract_inverted_index.this | 32, 37, 95, 167 |
| abstract_inverted_index.tree | 60 |
| abstract_inverted_index.used | 147 |
| abstract_inverted_index.work | 168 |
| abstract_inverted_index.(AB), | 64 |
| abstract_inverted_index.(DT), | 61 |
| abstract_inverted_index.(GB), | 71 |
| abstract_inverted_index.(GNB) | 76 |
| abstract_inverted_index.(LR), | 52 |
| abstract_inverted_index.(RF), | 55 |
| abstract_inverted_index.Bayes | 75 |
| abstract_inverted_index.Early | 18 |
| abstract_inverted_index.Naive | 74 |
| abstract_inverted_index.among | 128 |
| abstract_inverted_index.being | 88 |
| abstract_inverted_index.boost | 63 |
| abstract_inverted_index.eight | 45, 130 |
| abstract_inverted_index.false | 108, 112 |
| abstract_inverted_index.other | 136 |
| abstract_inverted_index.rate, | 110 |
| abstract_inverted_index.ratio | 153 |
| abstract_inverted_index.taken | 173 |
| abstract_inverted_index.under | 117 |
| abstract_inverted_index.using | 101 |
| abstract_inverted_index.which | 155, 178 |
| abstract_inverted_index.(FIR), | 154 |
| abstract_inverted_index.(KNN), | 58 |
| abstract_inverted_index.(SVM), | 68 |
| abstract_inverted_index.99.12% | 127 |
| abstract_inverted_index.breast | 6, 78, 82 |
| abstract_inverted_index.cancer | 4, 7, 79, 83 |
| abstract_inverted_index.causes | 13 |
| abstract_inverted_index.chosen | 145 |
| abstract_inverted_index.curve. | 118 |
| abstract_inverted_index.forest | 54 |
| abstract_inverted_index.method | 121 |
| abstract_inverted_index.random | 53 |
| abstract_inverted_index.spread | 30 |
| abstract_inverted_index.study. | 96 |
| abstract_inverted_index.vector | 66 |
| abstract_inverted_index.article | 38 |
| abstract_inverted_index.cancer. | 33 |
| abstract_inverted_index.dataset | 86 |
| abstract_inverted_index.leading | 12 |
| abstract_inverted_index.machine | 46, 67 |
| abstract_inverted_index.maximum | 124 |
| abstract_inverted_index.model's | 151, 176, 183 |
| abstract_inverted_index.outputs | 184 |
| abstract_inverted_index.studies | 138 |
| abstract_inverted_index.support | 65 |
| abstract_inverted_index.women's | 15 |
| abstract_inverted_index.American | 3 |
| abstract_inverted_index.Gaussian | 73 |
| abstract_inverted_index.accuracy | 125 |
| abstract_inverted_index.achieved | 122 |
| abstract_inverted_index.approach | 172 |
| abstract_inverted_index.boosting | 70 |
| abstract_inverted_index.compared | 134 |
| abstract_inverted_index.decision | 59 |
| abstract_inverted_index.experts. | 191 |
| abstract_inverted_index.features | 144 |
| abstract_inverted_index.findings | 93 |
| abstract_inverted_index.gradient | 69 |
| abstract_inverted_index.learning | 47 |
| abstract_inverted_index.logistic | 50 |
| abstract_inverted_index.metrics: | 105 |
| abstract_inverted_index.omission | 109 |
| abstract_inverted_index.society, | 5 |
| abstract_inverted_index.utilized | 89 |
| abstract_inverted_index.validate | 91 |
| abstract_inverted_index.According | 0 |
| abstract_inverted_index.K-nearest | 56 |
| abstract_inverted_index.Wisconsin | 84 |
| abstract_inverted_index.accuracy, | 106 |
| abstract_inverted_index.calculate | 149 |
| abstract_inverted_index.discovery | 113 |
| abstract_inverted_index.effective | 25 |
| abstract_inverted_index.following | 103 |
| abstract_inverted_index.including | 49 |
| abstract_inverted_index.indicates | 156 |
| abstract_inverted_index.mortality | 16 |
| abstract_inverted_index.neighbors | 57 |
| abstract_inverted_index.objective | 35 |
| abstract_inverted_index.treatment | 21 |
| abstract_inverted_index.algorithms | 131 |
| abstract_inverted_index.approaches | 26 |
| abstract_inverted_index.comparable | 137 |
| abstract_inverted_index.comparison | 43, 98 |
| abstract_inverted_index.detection. | 80 |
| abstract_inverted_index.healthcare | 190 |
| abstract_inverted_index.regression | 51 |
| abstract_inverted_index.sacrificed | 161 |
| abstract_inverted_index.uniqueness | 165 |
| abstract_inverted_index.worldwide. | 17 |
| abstract_inverted_index.algorithms, | 48 |
| abstract_inverted_index.literature. | 141 |
| abstract_inverted_index.performance | 104 |
| abstract_inverted_index.(diagnostic) | 85 |
| abstract_inverted_index.performance, | 177 |
| abstract_inverted_index.performance. | 163 |
| abstract_inverted_index.sensitivity, | 107 |
| abstract_inverted_index.specificity, | 111 |
| abstract_inverted_index.interpretable | 188 |
| abstract_inverted_index.explainability | 171 |
| abstract_inverted_index.identification | 19 |
| abstract_inverted_index.understandable | 186 |
| abstract_inverted_index.interpretability | 159 |
| abstract_inverted_index.fidelity-to-interpretability | 152 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.9200000166893005 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.95333836 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |