Machine Learning Assisted Cervical Cancer Detection

Bibliographic Data

ID22074704
AuthorsMavra Mehmood (Kinnaird College for Women University), Muhammad Rizwan (0000-0002-5332-9980, Kinnaird College for Women University), Michal Gregus (0000-0002-8156-8962, Comenius University Bratislava, corresponding author), Michal Gregus ml, Sidra Abbas (0000-0002-8204-4896, corresponding author)
Year2021
Volume9
Pages788376-788376
Publication date2021-12-23
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2021.788376
PMID35004588
OpenAlexW4200173872
LanguageEN
Citations received2
References cited41

Cervical malignant growth is the fourth most typical reason for disease demise in women around the globe. Cervical cancer growth is related to human papillomavirus (HPV) contamination. Early screening made cervical cancer a preventable disease that results in minimizing the global burden of cervical cancer. In developing countries, women do not approach sufficient screening programs because of the costly procedures to undergo examination regularly, scarce awareness, and lack of access to the medical center. In this manner, the expectation of the individual patient's risk becomes very high. There are many risk factors relevant to malignant cervical formation. This paper proposes an approach named CervDetect that uses machine learning algorithms to evaluate the risk elements of malignant cervical formation. CervDetect uses Pearson correlation between input variables as well as with the output variable to pre-process the data. CervDetect uses the random forest (RF) feature selection technique to select significant features. Finally, CervDetect uses a hybrid approach by combining RF and shallow neural networks to detect Cervical Cancer. Results show that CervDetect accurately predicts cervical cancer, outperforms the state-of-the-art studies, and achieved an accuracy of 93.6%, mean squared error (MSE) error of 0.07111, false-positive rate (FPR) of 6.4%, and false-negative rate (FNR) of 100

Cancer · Cervical cancer · Cervical screening · Colposcopy · Feature selection · Machine learning · Random forest · Statistics · AI in cancer detection · Cervical Cancer and HPV Research · Computer Science · Mathematics · Medicine · Artificial Intelligence · Internal Medicine

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