Building a Predictive Model to Assist in the Diagnosis of Cervical Cancer

References

• The Global Cancer Observatory 2021 (2021). www.gco.iarc.fr
   Google Scholar
• Alsmariy R , HealyG, AbdelhafezH. Predicting cervical cancer using machine learning methods. Int. J. Adv. Comput. Sci. Appl.11(7), 173–1842020.
   Google Scholar
• Kombe AJ , LiB, ZahidAet al. Epidemiology and burden of human papillomavirus and related diseases, molecular pathogenesis, and vaccine evaluation. Front Public Health8, 552028 (2020).
   PubMed Web of Science ®Google Scholar
• Alam Z , ShafieeHanjani L, DeanJet al. Cervical cancer screening among immigrant women residing in Australia: a systematic review. Asia Pac. J. Public Healthdoi:10.1177/10105395211006600 (2021) ( Epub ahead of print).
   Web of Science ®Google Scholar
• Gates A , PillayJ, ReynoldsDet al. Screening for the prevention and early detection of cervical cancer: protocol for systematic reviews to inform Canadian recommendations. Syst. Rev.10(1), 1–22 (2021).
   PubMed Web of Science ®Google Scholar
• Zhang X , ReyesMC. Pathologic Sampling Methods of the Cervix. In: Atlas of Diagnostic Pathology of the Cervix.SoslowRA, ParkKJ, StolnicuS(Eds). Springer, Cham, Switzerland (2021).
   Google Scholar
• Stolnicu S . Epithelial Malignant Tumors of the Cervix: Endocervical Adenocarcinoma. In: Atlas of Diagnostic Pathology of the Cervix.SoslowRA, ParkKJ, StolnicuS, (Eds). Springer, Cham, Switzerland (2021).
   Google Scholar
• Hull R , MbeleM, MakhafolaTet al. Cervical cancer in low and middle-income countries. Oncol. Lett.20(3), 2058–2074 (2020).
   PubMed Web of Science ®Google Scholar
• Serarslan A , MeydanD, YıldızRE. Radical radiotherapy of locally advanced cervix uteri carcinoma. Cancer Biol. Ther.2(1), 8–14 (2021).
   Google Scholar
• Bhatla N , AokiD, SharmaDNet al. Cancer of the cervix uteri. Int. J. Gynaecol. Obstet.143, 22–36 (2018).
   PubMed Web of Science ®Google Scholar
• Reed N , BalegaJ, BarwickTet al. British Gynaecological Cancer Society (BGCS) cervical cancer guidelines: recommendations for practice. Eur. J. Obstet. Gynecol. Reprod. Biol.256, 433–465 (2020).
   PubMed Web of Science ®Google Scholar
• Zhang S , XuH, ZhangLet al. Cervical cancer: epidemiology, risk factors and screening. Chin. J. Cancer Res.32(6), 720 (2020).
   PubMed Web of Science ®Google Scholar
• Momenimovahed Z , SalehiniyaH. Incidence, mortality and risk factors of cervical cancer in the world. Biomed. Res. Ther.4(12), 1795–1811 (2017).
   Web of Science ®Google Scholar
• Stewart TS , MoodleyJ, WalterFM. Population risk factors for late-stage presentation of cervical cancer in sub-Saharan Africa. Cancer Epidemiol.53, 81–92 (2018).
   PubMed Web of Science ®Google Scholar
• Kerkar RA , KulkarniYV. Screening for cervical cancer: an overview. J. Obstet. Gynecol. India56(2), 115–222 (2006).
   Google Scholar
• Guvenc G , AkyuzA, AçikelCH. Health belief model scale for cervical cancer and pap smear test: psychometric testing. J. Adv. Nurs.67(2), 428–437 (2011).
   PubMed Web of Science ®Google Scholar
• Galgano MT , CastlePE, AtkinsKAet al. Using biomarkers as objective standards in the diagnosis of cervical biopsies. Am. J. Surg. Pathol.34(8), 1077 (2010).
   PubMed Web of Science ®Google Scholar
• Ramaraju H , NagaveniY, KhaziA. Use of Schiller's test versus pap smear to increase the detection rate of cervical dysplasias. Int. J. Reprod. Contracept. Obstet. Gynecol.5, 1446–1450 (2017).
   Google Scholar
• McVeigh PZ , SyedAM, MilosevicMet al. Diffusion-weighted MRI in cervical cancer. Eur. Radiol.18(5), 1058–1064 (2008).
   PubMed Web of Science ®Google Scholar
• Exner M , KuehnA, StumppPet al. Value of diffusion-weighted MRI in diagnosis of uterine cervical cancer: a prospective study evaluating the benefits of DWI compared to conventional MR sequences in a 3T environment. Acta Radiol.57(7), 869–877 (2016).
   PubMed Web of Science ®Google Scholar
• Tseng CJ , LuCJ, ChangCCet al. Application of machine learning to predict the recurrence-proneness for cervical cancer. Neural Comput. Appl.24(6), 1311–1316 (2014).
   Web of Science ®Google Scholar
• Fatlawi HK . Enhanced classification model for cervical cancer dataset based on cost sensitive classifier. Int. J. Comput. Tech.4(4), 115–120 (2017).
   Google Scholar
• Chang CC , ChengSL, LuCJet al. Prediction of recurrence in patients with cervical cancer using mars and classification. Int. J. Mach. Learn Comput.3(1), 75 (2013).
   Google Scholar
• Alam TM , KhanMM, IqbalMAet al. Cervical cancer prediction through different screening methods using data mining. Int. J. Adv. Comput. Sci. Appl.10(2), (2019).
   Google Scholar
• Zhang J , LiuY. Cervical cancer detection using SVM based feature screening. InInternational Conference on Medical Image Computing and Computer-Assisted Intervention.BarillotC, HaynorDR, HellierP(Eds). Springer, Berlin, Heidelberg (2004).
   Google Scholar
• Ghoneim A , MuhammadG, HossainMS. Cervical cancer classification using convolutional neural networks and extreme learning machines. Future Gener. Comput. Syst.102, 643–649 (2020).
   Web of Science ®Google Scholar
• Mukhopadhyay S , KurmiI, DeyRet al. Optical diagnosis of colon and cervical cancer by support vector machine. Biophotonics9887, 98870U (2016).
   Google Scholar
• Nematollahi M , AkbariR, NikeghbalianS, SalehnasabC. Classification models to predict survival of kidney transplant recipients using two intelligent techniques of data mining and logistic regression. Int. J. Organ Transplant Med.8(2), 119 (2017).
   PubMedGoogle Scholar
• Rezaianzadeh A , DastoorpoorM, SanaeiMet al. Predictors of length of stay in the coronary care unit in patient with acute coronary syndrome based on data mining methods. Clin. Epidemiol. Glob. Health8(2), 383–388 (2020).
   Web of Science ®Google Scholar
• Kusy M , ObrzutB, KluskaJ. Application of gene expression programming and neural networks to predict adverse events of radical hysterectomy in cervical cancer patients. Med. Biol. Eng. Comput.51(12), 1357–1365 (2013).
   PubMed Web of Science ®Google Scholar
• Sokouti B , HaghipourS, TabriziAD. A framework for diagnosing cervical cancer disease based on feedforward MLP neural network and Thin Prep histopathological cell image features. Neural Comput. Appl.24(1), 221–232 (2014).
   Web of Science ®Google Scholar
• Qiu X , TaoN, TanY, WuX. Constructing of the risk classification model of cervical cancer by artificial neural network. Expert Syst. Appl.32(4), 1094–1099 (2007).
   Web of Science ®Google Scholar
• Benazir B , NagarajanA. An expert system for predicting the cervical cancer using data mining techniques. Int. J. Pure Appl. Math.118(20), 1971–1987 (2018).
   Google Scholar
• Lu J , SongE, GhoneimA, AlrashoudM. Machine learning for assisting cervical cancer diagnosis: an ensemble approach. Future Gener. Comput. Syst.106, 199–205 (2020).
   Web of Science ®Google Scholar
• Jangra A , DeswalA. An Efficient Algorithm for Early Diagnosis of Cervical Cancer Using Random Forest Classifier. In: Advances in Computational Intelligence Techniques.JainS, SoodM, PaulS. (Eds). Springer, Singapore (2020).
   Google Scholar
• Cortes C , VapnikVN. Support vector networks. Mach. Learn.20(3), 273–297 (1995).
   Web of Science ®Google Scholar
• Sommer C . Quantitative characterization, classification and reconstruction of oocyst shapes of Eimeria species from cattle. Parasitol116(1), 21–28 (1998).
   PubMedGoogle Scholar
• Burges CJ . A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov.2(2), 121–167 (1998).
   Web of Science ®Google Scholar
• Burges CJ , SmolaAJ, ScholkopfB. Advances in kernel methods. Support Vector Learning.MIT press, London, UK (1999).
   Google Scholar
• Haykin S . Neural Networks: A Comprehensive Foundation.Prentice Hall PTR, Upper Saddle River, NJ, USA, 7458, 161–175 (1999).
   Google Scholar
• West D . Neural network credit scoring models. Comput. Oper. Res.27(11–12), 1131–1152 (2000).
   Web of Science ®Google Scholar
• Deng X , LuoY, WangC. Analysis of risk factors for cervical cancer based on machine learning methods. 5th IEEE Int. Con. Cloud Comput. Intel Systs.2018, 631–635 (2018).
   Google Scholar
• Hossain MS , AminSU, AlsulaimanM, MuhammadG. Applying deep learning for epilepsy seizure detection and brain mapping visualization. ACM Trans. Multi. Comput. Comm. Appl.15(Suppl. 1), 1–17 (2019).
   Google Scholar
• Chen M , ShiX, ZhangY, WuD, GuizaniM. Deep features learning for medical image analysis with convolutional autoencoder neural network. IEEE Trans. Big Data.7(4), 750–758 (2017).
   Google Scholar
• Singh Y , SrivastavaD, ChandranandP, SinghS. https://arxiv.org/abs/1811.00849 (2018).
   Google Scholar
• Vamathevan J , ClarkD, CzodrowskiPet al. Applications of machine learning in drug discovery and development. Nat. Rev. Drug. Discov.18(6), 463–477 (2019).
   PubMed Web of Science ®Google Scholar
• Erickson BJ , KorfiatisP, AkkusZ, KlineTL. Machine learning for medical imaging. Radiographics37(2), 505–515 (2017).
   PubMed Web of Science ®Google Scholar
• Bakator M , RadosavD. Deep learning and medical diagnosis: a review of literature. Multi. Tech. Inter.2(3), 47 (2018).
   Google Scholar
• Chen M , YangJ, ZhouJet al. 5G-smart diabetes: toward personalized diabetes diagnosis with healthcare big data clouds. IEEE Commun. Mag.56(4), 16–23 (2018).
   Web of Science ®Google Scholar
• Chen M , HaoY, HwangKet al. Disease prediction by machine learning over big data from healthcare communities. IEEE Access5, 8869–8879 (2017).
   Web of Science ®Google Scholar
• Chen M , ZhangY, QiuMet al. SPHA: smart personal health advisor based on deep analytics. IEEE Commun. Mag.56(3), 164–169 (2018).
   Web of Science ®Google Scholar
• Pogue BW , MycekMA, HarperDM. Image analysis for discrimination of cervical neoplasia. J. Biomed Opt.5(1), 72–82 (2000).
   PubMed Web of Science ®Google Scholar
• Cancer Genome Atlas Research Network . Integrated genomic and molecular characterization of cervical cancer. Nature543(7645), 378 (2017).
   PubMed Web of Science ®Google Scholar
• Audirac-Chalifour A , Torres-PovedaK, Bahena-RománMet al. Cervical microbiome and cytokine profile at various stages of cervical cancer: a pilot study. PLoS ONE1(4), e0153274 (2016).
   Google Scholar
• Ashraf FB , MomoNS. Comparative analysis on prediction models with various data preprocessings in the prognosis of cervical cancer. Inter. Conf. Comput. Commun. Netwrk Tech (ICCCNT)2019, 1–6 (2019).
   Google Scholar
• Fernandes K , ChiccoD, CardosoJS, FernandesJ. Supervised deep learning embeddings for the prediction of cervical cancer diagnosis. Peer J. Comput. Sci.4, e154 (2018).
   PubMedGoogle Scholar
• Hu L , BellD, AntaniSet al. An observational study of deep learning and automated evaluation of cervical images for cancer screening. J. Natl Cancer Inst.111(9), 923–932 (2019).
   PubMedGoogle Scholar
• William W , WareA, Basaza-EjiriAH, ObungolochJ. A review of image analysis and machine learning techniques for automated cervical cancer screening from pap-smear images. Comput. Methods Programs Biomed.164, 15–22 (2018).
   PubMed Web of Science ®Google Scholar
• Asadi F , SalehnasabC, AjoriL. Supervised algorithms of machine learning for the prediction of cervical cancer. J. Biomed. Phys. Eng.10(4), 513 (2020).
   PubMedGoogle Scholar
• Zhu CR , ByrdH, NocedalJ. L-BFGS-B: Algorithm 778: L-BFGS-B, FORTRAN routines for large scale bound constrained optimization. ACM Trans. Math Softw.23(4), 550–560 (1997).
   Web of Science ®Google Scholar
• Rokach L . “Decision forest: twenty years of research”. Info Fusionv27, 111–125 (2016).
   Google Scholar
• Friedman JH . Greedy function approximation: a gradient boosting machine. Annls Stats.29(5), 1189–1232 (2001).
   Web of Science ®Google Scholar
• Kramer O . K-nearest neighbors. In: Dimensionality Reduction with Unsupervised Nearest Neighbors.Springer, Berlin, Heidelberg (2013).
   Google Scholar
• Cheng S , WangL, ZhangP. Supervised learning with projected entangled pair states. Phy. Rev. B.103(12), 125117 (2021).
   Web of Science ®Google Scholar
• Tax DM , Van BreukelenM, DuinRP, KittlerJ. Combining multiple classifiers by averaging or by multiplying?Patt. Recog.33(9), 1475–1485 (2000).
   Web of Science ®Google Scholar