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. 2022 May 29;22(11):4132.
doi: 10.3390/s22114132.

A Model for Predicting Cervical Cancer Using Machine Learning Algorithms

Naif Al Mudawi  1 , Abdulwahab Alazeb  1
Affiliations

A Model for Predicting Cervical Cancer Using Machine Learning Algorithms

Naif Al Mudawi et al. Sensors (Basel). .

Abstract

A growing number of individuals and organizations are turning to machine learning (ML) and deep learning (DL) to analyze massive amounts of data and produce actionable insights. Predicting the early stages of serious illnesses using ML-based schemes, including cancer, kidney failure, and heart attacks, is becoming increasingly common in medical practice. Cervical cancer is one of the most frequent diseases among women, and early diagnosis could be a possible solution for preventing this cancer. Thus, this study presents an astute way to predict cervical cancer with ML algorithms. Research dataset, data pre-processing, predictive model selection (PMS), and pseudo-code are the four phases of the proposed research technique. The PMS section reports experiments with a range of classic machine learning methods, including decision tree (DT), logistic regression (LR), support vector machine (SVM), K-nearest neighbors algorithm (KNN), adaptive boosting, gradient boosting, random forest, and XGBoost. In terms of cervical cancer prediction, the highest classification score of 100% is achieved with random forest (RF), decision tree (DT), adaptive boosting, and gradient boosting algorithms. In contrast, 99% accuracy has been found with SVM. The computational complexity of classic machine learning techniques is computed to assess the efficacy of the models. In addition, 132 Saudi Arabian volunteers were polled as part of this study to learn their thoughts about computer-assisted cervical cancer prediction, to focus attention on the human papillomavirus (HPV).

Keywords: cervical cancer; gradient boosting; human papillomavirus (HPV); machine learning (ML); support vector machine (SVM).

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Proposed research model for classifying cervical cancer.
Figure 2
Figure 2
Correlations between different variables of cervical cancer.
Figure 3
Figure 3
Count measurement in terms of the number of pregnancies, number of sexual partners, and age.
Figure 4
Figure 4
Visualization of comparison between biopsy and number of pregnancies.
Figure 5
Figure 5
Number of responses regarding the awareness of human papillomavirus (HPV).
Figure 6
Figure 6
Survey responses regarding whether or not the rate of being affected by cervical cancer is becoming higher than before.
Figure 7
Figure 7
Total percentage of individuals who have undergone a biopsy test or another cervical cancer (uterus)-related test before.
Figure 8
Figure 8
The awareness level in rural and urban areas regarding cervical cancer.
See this image and copyright information in PMC

References

    1. Martin C.M., Astbury K., McEvoy L., Toole S., Sheils O., Leary J.J. Inflammation and Cancer. Volume 511. Springer; Berlin, Germany: 2009. Gene expression profiling in cervical cancer: Identification of novel markers for disease diagnosis and therapy; pp. 333–359. - PubMed
    1. Purnami S., Khasanah P., Sumartini S., Chosuvivatwong V., Sriplung H. Cervical cancer survival prediction using hybrid of SMOTE, CART and smooth support vector machine. AIP Conf. Proc. 2016;1723:030017.
    1. Yang X., Da M., Zhang W., Qi Q., Zhang C., Han S. Role of lactobacillus in cervical cancer. Cancer Manag. Res. 2018;10:1219–1229. doi: 10.2147/CMAR.S165228. - DOI - PMC - PubMed
    1. Ghoneim A., Muhammad G., Hossain M.S. Cervical cancer classification using convolutional neural networks and extreme learning machines. Future Gener. Comput. Syst. 2020;102:643–649. doi: 10.1016/j.future.2019.09.015. - DOI
    1. Rehman O., Zhuang H., Muhamed Ali A., Ibrahim A., Li Z. Validation of miRNAs as breast cancer biomarkers with a machine learning approach. Cancers. 2019;11:431. doi: 10.3390/cancers11030431. - DOI - PMC - PubMed