Hybrid Model for Detection of Cervical Cancer Using Causal Analysis and Machine Learning Techniques
<div><p>Cervical cancer has become the third most common form of cancer in the in-universe, after the widespread breast cancer. Human papillomavirus risk of infection is linked to the majority of cancer cases. Preventive care, the most expensive way of fighting cancer, can protect about...
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| مؤلفون آخرون: | , , , , , , , |
| منشور في: |
2022
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| _version_ | 1864513519263678464 |
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| author | Umesh Kumar Lilhore (17727684) |
| author2 | M. Poongodi (14158869) Amandeep Kaur (572773) Sarita Simaiya (17727693) Abeer D. Algarni (18288967) Hela Elmannai (18288970) V. Vijayakumar (9544795) Godwin Brown Tunze (18300823) Mounir Hamdi (14150652) |
| author2_role | author author author author author author author author |
| author_facet | Umesh Kumar Lilhore (17727684) M. Poongodi (14158869) Amandeep Kaur (572773) Sarita Simaiya (17727693) Abeer D. Algarni (18288967) Hela Elmannai (18288970) V. Vijayakumar (9544795) Godwin Brown Tunze (18300823) Mounir Hamdi (14150652) |
| author_role | author |
| dc.creator.none.fl_str_mv | Umesh Kumar Lilhore (17727684) M. Poongodi (14158869) Amandeep Kaur (572773) Sarita Simaiya (17727693) Abeer D. Algarni (18288967) Hela Elmannai (18288970) V. Vijayakumar (9544795) Godwin Brown Tunze (18300823) Mounir Hamdi (14150652) |
| dc.date.none.fl_str_mv | 2022-05-04T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1155/2022/4688327 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Hybrid_Model_for_Detection_of_Cervical_Cancer_Using_Causal_Analysis_and_Machine_Learning_Techniques/25539595 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biological sciences Genetics Biomedical and clinical sciences Immunology Mathematical sciences Applied mathematics Cervical Cancer Causal Analysis Machine Learning Techniques |
| dc.title.none.fl_str_mv | Hybrid Model for Detection of Cervical Cancer Using Causal Analysis and Machine Learning Techniques |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <div><p>Cervical cancer has become the third most common form of cancer in the in-universe, after the widespread breast cancer. Human papillomavirus risk of infection is linked to the majority of cancer cases. Preventive care, the most expensive way of fighting cancer, can protect about 37% of cancer cases. The Pap smear examination is a standard screening procedure for the initial screening of cervical cancer. However, this manual test procedure generates many false-positive outcomes due to individual errors. Various researchers have extensively investigated machine learning (ML) methods for classifying cervical Pap cells to enhance manual testing. The random forest method is the most popular method for anticipating features from a high-dimensional cancer image dataset. However, the random forest method can get too slow and inefficient for real-time forecasts when too many decision trees are used. This research proposed an efficient feature selection and prediction model for cervical cancer datasets using Boruta analysis and SVM method to deal with this challenge. A Boruta analysis method is used. It is improved from of random forest method and mainly discovers feature subsets from the data source that are significant to assigned classification activity. The proposed model’s primary aim is to determine the importance of cervical cancer screening factors for classifying high-risk patients depending on the findings. This research work analyses cervical cancer and various risk factors to help detect cervical cancer. The proposed model Boruta with SVM and various popular ML models are implemented using Python and various performance measuring parameters, i.e., accuracy, precision, F 1 – Score , and recall. However, the proposed Boruta analysis with SVM performs outstanding over existing methods.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Computational and Mathematical Methods in Medicine<br> License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1155/2022/4688327" target="_blank">https://dx.doi.org/10.1155/2022/4688327</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_742c4bc55e30eed0307053a01e677b74 |
| identifier_str_mv | 10.1155/2022/4688327 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25539595 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Hybrid Model for Detection of Cervical Cancer Using Causal Analysis and Machine Learning TechniquesUmesh Kumar Lilhore (17727684)M. Poongodi (14158869)Amandeep Kaur (572773)Sarita Simaiya (17727693)Abeer D. Algarni (18288967)Hela Elmannai (18288970)V. Vijayakumar (9544795)Godwin Brown Tunze (18300823)Mounir Hamdi (14150652)Biological sciencesGeneticsBiomedical and clinical sciencesImmunologyMathematical sciencesApplied mathematicsCervical CancerCausal AnalysisMachine LearningTechniques<div><p>Cervical cancer has become the third most common form of cancer in the in-universe, after the widespread breast cancer. Human papillomavirus risk of infection is linked to the majority of cancer cases. Preventive care, the most expensive way of fighting cancer, can protect about 37% of cancer cases. The Pap smear examination is a standard screening procedure for the initial screening of cervical cancer. However, this manual test procedure generates many false-positive outcomes due to individual errors. Various researchers have extensively investigated machine learning (ML) methods for classifying cervical Pap cells to enhance manual testing. The random forest method is the most popular method for anticipating features from a high-dimensional cancer image dataset. However, the random forest method can get too slow and inefficient for real-time forecasts when too many decision trees are used. This research proposed an efficient feature selection and prediction model for cervical cancer datasets using Boruta analysis and SVM method to deal with this challenge. A Boruta analysis method is used. It is improved from of random forest method and mainly discovers feature subsets from the data source that are significant to assigned classification activity. The proposed model’s primary aim is to determine the importance of cervical cancer screening factors for classifying high-risk patients depending on the findings. This research work analyses cervical cancer and various risk factors to help detect cervical cancer. The proposed model Boruta with SVM and various popular ML models are implemented using Python and various performance measuring parameters, i.e., accuracy, precision, F 1 – Score , and recall. However, the proposed Boruta analysis with SVM performs outstanding over existing methods.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Computational and Mathematical Methods in Medicine<br> License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1155/2022/4688327" target="_blank">https://dx.doi.org/10.1155/2022/4688327</a></p>2022-05-04T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1155/2022/4688327https://figshare.com/articles/journal_contribution/Hybrid_Model_for_Detection_of_Cervical_Cancer_Using_Causal_Analysis_and_Machine_Learning_Techniques/25539595CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/255395952022-05-04T03:00:00Z |
| spellingShingle | Hybrid Model for Detection of Cervical Cancer Using Causal Analysis and Machine Learning Techniques Umesh Kumar Lilhore (17727684) Biological sciences Genetics Biomedical and clinical sciences Immunology Mathematical sciences Applied mathematics Cervical Cancer Causal Analysis Machine Learning Techniques |
| status_str | publishedVersion |
| title | Hybrid Model for Detection of Cervical Cancer Using Causal Analysis and Machine Learning Techniques |
| title_full | Hybrid Model for Detection of Cervical Cancer Using Causal Analysis and Machine Learning Techniques |
| title_fullStr | Hybrid Model for Detection of Cervical Cancer Using Causal Analysis and Machine Learning Techniques |
| title_full_unstemmed | Hybrid Model for Detection of Cervical Cancer Using Causal Analysis and Machine Learning Techniques |
| title_short | Hybrid Model for Detection of Cervical Cancer Using Causal Analysis and Machine Learning Techniques |
| title_sort | Hybrid Model for Detection of Cervical Cancer Using Causal Analysis and Machine Learning Techniques |
| topic | Biological sciences Genetics Biomedical and clinical sciences Immunology Mathematical sciences Applied mathematics Cervical Cancer Causal Analysis Machine Learning Techniques |