SKCV: Stratified K-fold cross-validation on ML classifiers for predicting cervical cancer
Cancer is the unregulated development of abnormal cells in the human body system. Cervical cancer, also known as cervix cancer, develops on the cervix’s surface. This causes an overabundance of cells to build up, eventually forming a lump or tumour. As a result, early detection is essential to deter...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-08-01
|
| Series: | Frontiers in Nanotechnology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnano.2022.972421/full |
| _version_ | 1811339875344973824 |
|---|---|
| author | Sashikanta Prusty Srikanta Patnaik Sujit Kumar Dash |
| author_facet | Sashikanta Prusty Srikanta Patnaik Sujit Kumar Dash |
| author_sort | Sashikanta Prusty |
| collection | DOAJ |
| description | Cancer is the unregulated development of abnormal cells in the human body system. Cervical cancer, also known as cervix cancer, develops on the cervix’s surface. This causes an overabundance of cells to build up, eventually forming a lump or tumour. As a result, early detection is essential to determine what effective treatment we can take to overcome it. Therefore, the novel Machine Learning (ML) techniques come to a place that predicts cervical cancer before it becomes too serious. Furthermore, four common diagnosis testing namely, Hinselmann, Schiller, Cytology, and Biopsy have been compared and predicted with four common ML models, namely Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (K-NNs), and Extreme Gradient Boosting (XGB). Additionally, to enhance the better performance of ML models, the Stratified k-fold cross-validation (SKCV) method has been implemented over here. The findings of the experiments demonstrate that utilizing an RF classifier for analyzing the cervical cancer risk, could be a good alternative for assisting clinical specialists in classifying this disease in advance. |
| first_indexed | 2024-04-13T18:34:05Z |
| format | Article |
| id | doaj.art-4fd7860d4da14d6fa947416c40be5f94 |
| institution | Directory Open Access Journal |
| issn | 2673-3013 |
| language | English |
| last_indexed | 2024-04-13T18:34:05Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Nanotechnology |
| spelling | doaj.art-4fd7860d4da14d6fa947416c40be5f942022-12-22T02:34:58ZengFrontiers Media S.A.Frontiers in Nanotechnology2673-30132022-08-01410.3389/fnano.2022.972421972421SKCV: Stratified K-fold cross-validation on ML classifiers for predicting cervical cancerSashikanta Prusty0Srikanta Patnaik1Sujit Kumar Dash2Department of Computer Science & Engineering, Siksha “O” Anusandhan (Deemed to be University), Bhubaneswar, IndiaDepartment of Computer Science & Engineering, Siksha “O” Anusandhan (Deemed to be University), Bhubaneswar, IndiaDepartment of Electrical & Electronics Engineering, Siksha “O” Anusandhan (Deemed to be University), Bhubaneswar, IndiaCancer is the unregulated development of abnormal cells in the human body system. Cervical cancer, also known as cervix cancer, develops on the cervix’s surface. This causes an overabundance of cells to build up, eventually forming a lump or tumour. As a result, early detection is essential to determine what effective treatment we can take to overcome it. Therefore, the novel Machine Learning (ML) techniques come to a place that predicts cervical cancer before it becomes too serious. Furthermore, four common diagnosis testing namely, Hinselmann, Schiller, Cytology, and Biopsy have been compared and predicted with four common ML models, namely Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (K-NNs), and Extreme Gradient Boosting (XGB). Additionally, to enhance the better performance of ML models, the Stratified k-fold cross-validation (SKCV) method has been implemented over here. The findings of the experiments demonstrate that utilizing an RF classifier for analyzing the cervical cancer risk, could be a good alternative for assisting clinical specialists in classifying this disease in advance.https://www.frontiersin.org/articles/10.3389/fnano.2022.972421/fullcervical histogram imagesMLSKCVROC AUCprperformance measure |
| spellingShingle | Sashikanta Prusty Srikanta Patnaik Sujit Kumar Dash SKCV: Stratified K-fold cross-validation on ML classifiers for predicting cervical cancer Frontiers in Nanotechnology cervical histogram images ML SKCV ROC AUC pr performance measure |
| title | SKCV: Stratified K-fold cross-validation on ML classifiers for predicting cervical cancer |
| title_full | SKCV: Stratified K-fold cross-validation on ML classifiers for predicting cervical cancer |
| title_fullStr | SKCV: Stratified K-fold cross-validation on ML classifiers for predicting cervical cancer |
| title_full_unstemmed | SKCV: Stratified K-fold cross-validation on ML classifiers for predicting cervical cancer |
| title_short | SKCV: Stratified K-fold cross-validation on ML classifiers for predicting cervical cancer |
| title_sort | skcv stratified k fold cross validation on ml classifiers for predicting cervical cancer |
| topic | cervical histogram images ML SKCV ROC AUC pr performance measure |
| url | https://www.frontiersin.org/articles/10.3389/fnano.2022.972421/full |
| work_keys_str_mv | AT sashikantaprusty skcvstratifiedkfoldcrossvalidationonmlclassifiersforpredictingcervicalcancer AT srikantapatnaik skcvstratifiedkfoldcrossvalidationonmlclassifiersforpredictingcervicalcancer AT sujitkumardash skcvstratifiedkfoldcrossvalidationonmlclassifiersforpredictingcervicalcancer |