Automated invasive cervical cancer disease detection at early stage through suitable machine learning model
Abstract Cervical cancer is a common cancer that affects women all over the world. This is the fourth leading cause of death among women and has no symptoms in its early stages. At the cervix, cervical cancer cells develop slowly. If it can be detected early, this cancer can be successfully treated....
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doaj-fb88045b2fbc48729743b13d79ffabe42021-09-19T11:20:24ZengSpringerSN Applied Sciences2523-39632523-39712021-09-0131011710.1007/s42452-021-04786-zAutomated invasive cervical cancer disease detection at early stage through suitable machine learning modelSohely Jahan0M. D. Saimun Islam1Linta Islam2Tamanna Yesmin Rashme3Ayesha Aziz Prova4Bikash Kumar Paul5M. D. Manowarul Islam6Mohammed Khaled Mosharof7Department of Computer Science and Engineering, University of BarisalDepartment of Computer Science and Engineering, University of BarisalDepartment of Computer Science and Engineering, Jagannath UniversityDepartment of Computer Science and Engineering, Uttara UniversityDepartment of Computer Science and Engineering, Central Women’s UniversityDepartment of Software Engineering (SWE), Daffodil International University (DIU)Department of Computer Science and Engineering, Jagannath UniversityDepartment of ICT, ICT Division, Telecommunication & Information TechnologyAbstract Cervical cancer is a common cancer that affects women all over the world. This is the fourth leading cause of death among women and has no symptoms in its early stages. At the cervix, cervical cancer cells develop slowly. If it can be detected early, this cancer can be successfully treated. Health professionals are now facing a major challenge in detecting such cancer until it spreads rapidly. This study applied various machine learning classification methods to predict cervical cancer using risk factors. The main aim of this research work is to be described of the performance variation of eight most classifications algorithm to detect cervical cancer disease based on the selection of various top features sets from the dataset. Multilayer Perceptron (MLP), Random Forest and k-Nearest Neighbor, Decision Tree, Logistic Regression, SVC, Gradient Boosting, AdaBoost are examples of machine learning classification algorithms that have been used to predict cervical cancer and help in early diagnosis. A variety of approaches are used to avoid missing values in the dataset. To choose the various best features, a combination of feature selection techniques such as Chi-square, SelectBest and Random Forest was used. The performance of those classifications is evaluated using the accuracy, recall, precision and f1-score parameters. On a variety of top feature sets, MLP outperformed other classification models. The majority of classification models, on the other hand, claim to have the highest accuracy on the top 25 features in dataset splitting ratio (70:30). For each model, the percentage of correctly classified instances has been presented and all of the results are then discussed. Medical professionals will be able to use the suggested approach to perform research on cervical cancer.https://doi.org/10.1007/s42452-021-04786-zCervical cancerClassificationEarly-stage detectionFeatures selectionSVCMultilayer perceptron |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sohely Jahan M. D. Saimun Islam Linta Islam Tamanna Yesmin Rashme Ayesha Aziz Prova Bikash Kumar Paul M. D. Manowarul Islam Mohammed Khaled Mosharof |
spellingShingle |
Sohely Jahan M. D. Saimun Islam Linta Islam Tamanna Yesmin Rashme Ayesha Aziz Prova Bikash Kumar Paul M. D. Manowarul Islam Mohammed Khaled Mosharof Automated invasive cervical cancer disease detection at early stage through suitable machine learning model SN Applied Sciences Cervical cancer Classification Early-stage detection Features selection SVC Multilayer perceptron |
author_facet |
Sohely Jahan M. D. Saimun Islam Linta Islam Tamanna Yesmin Rashme Ayesha Aziz Prova Bikash Kumar Paul M. D. Manowarul Islam Mohammed Khaled Mosharof |
author_sort |
Sohely Jahan |
title |
Automated invasive cervical cancer disease detection at early stage through suitable machine learning model |
title_short |
Automated invasive cervical cancer disease detection at early stage through suitable machine learning model |
title_full |
Automated invasive cervical cancer disease detection at early stage through suitable machine learning model |
title_fullStr |
Automated invasive cervical cancer disease detection at early stage through suitable machine learning model |
title_full_unstemmed |
Automated invasive cervical cancer disease detection at early stage through suitable machine learning model |
title_sort |
automated invasive cervical cancer disease detection at early stage through suitable machine learning model |
publisher |
Springer |
series |
SN Applied Sciences |
issn |
2523-3963 2523-3971 |
publishDate |
2021-09-01 |
description |
Abstract Cervical cancer is a common cancer that affects women all over the world. This is the fourth leading cause of death among women and has no symptoms in its early stages. At the cervix, cervical cancer cells develop slowly. If it can be detected early, this cancer can be successfully treated. Health professionals are now facing a major challenge in detecting such cancer until it spreads rapidly. This study applied various machine learning classification methods to predict cervical cancer using risk factors. The main aim of this research work is to be described of the performance variation of eight most classifications algorithm to detect cervical cancer disease based on the selection of various top features sets from the dataset. Multilayer Perceptron (MLP), Random Forest and k-Nearest Neighbor, Decision Tree, Logistic Regression, SVC, Gradient Boosting, AdaBoost are examples of machine learning classification algorithms that have been used to predict cervical cancer and help in early diagnosis. A variety of approaches are used to avoid missing values in the dataset. To choose the various best features, a combination of feature selection techniques such as Chi-square, SelectBest and Random Forest was used. The performance of those classifications is evaluated using the accuracy, recall, precision and f1-score parameters. On a variety of top feature sets, MLP outperformed other classification models. The majority of classification models, on the other hand, claim to have the highest accuracy on the top 25 features in dataset splitting ratio (70:30). For each model, the percentage of correctly classified instances has been presented and all of the results are then discussed. Medical professionals will be able to use the suggested approach to perform research on cervical cancer. |
topic |
Cervical cancer Classification Early-stage detection Features selection SVC Multilayer perceptron |
url |
https://doi.org/10.1007/s42452-021-04786-z |
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