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Prediction of Cervical Cancer from Behavior Risk Using Machine Learning Techniques

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Abstract

Cervical cancer growth is the fourth maximum of regular diseases in females. It is one of the sicknesses which is compromising ladies' wellbeing everywhere in the world and it is difficult to notice any sign in the beginning phase. But the screening process of cervical cancer sometimes is being hampered due to some social-behavioral factors. There is still a limited number of researches directed in cervical cancer identification dependent on the behavior and machine learning in the area of gynecology and computer science. In this research, we have proposed three machine learning models such as Decision Tree, Random Forest, and XGBoost to predict cervical cancer from behavior and its variables and we got significantly improved outcomes than the current methods with 93.33% accuracy. Moreover, we have shown the top features from the dataset according to the feature important scores to know their impacts on the development of the classification model.

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References

  1. Ferlay J, Shin H, Bray F, Forman D, Mathers C, Parkin D. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer. 2010;127(12):2893–917.

    Article  Google Scholar 

  2. Guidelines for cervical cancer screening programme. Chandigarh: Department of Cytology & Gynaecological Pathology, Postgraduate Institute of Medical Education, Research, screening.iarc.fr, 2020. Accessed 29 Oct 2020.

  3. Ndikom C, Ofi B. Awareness, perception and factors affecting utilization of cervical cancer screening services among women in Ibadan, Nigeria: a qualitative study. Reprod Health. 2012;9:1–8.

    Article  Google Scholar 

  4. Hussain S, Sullivan R. Cancer control in Bangladesh. Jpn J Clin Oncol. 2013;43(12):1159–69.

    Article  Google Scholar 

  5. Paul BS. Studies on the epidemiology of cervical cancer in Southern Assam. Assam Univ J Sci Technol. 2011;7(1):36–42.

    Google Scholar 

  6. Deng X, Luo Y., Wang C. Analysis of risk factors for cervical cancer based on machine learning methods. In: Proc. of 5th IEEE international conference on cloud computing and intelligence systems (CCIS), Nanjing, China, 2018. p. 631–5.

  7. Lu J, Song E, Ghoneim A, Alrashoud M. Machine learning for assisting cervical cancer diagnosis: an ensemble approach. Futur Gener Comput Syst. 2020;106:199–205.

    Article  Google Scholar 

  8. Nithya B, Ilango V. Evaluation of machine learning based optimized feature selection approaches and classification methods for cervical cancer prediction. SN Appl Sci. 2019;1(6):1–16.

    Article  Google Scholar 

  9. Parikh D, Menon V. Machine learning applied to cervical cancer data. Int J Math Sci Comput. 2019;5(1):53–64.

    Google Scholar 

  10. Tseng C, Lu C, Chang C, Chen G. Application of machine learning to predict the recurrence-proneness for cervical cancer. Neural Comput Appl. 2013;24(6):1311–6.

    Article  Google Scholar 

  11. Suman S, Hooda N. Predicting risk of cervical cancer: a case study of machine learning. J Stat Manag Syst. 2019;22(4):689–96.

    Google Scholar 

  12. UCI machine learning repository: cervical cancer behavior risk data set. Archive.ics.uci.edu, 2020. Accessed 10 Nov 2020.

  13. Machmud R, Wijaya A. Behavior determinant based cervical cancer early detection with machine learning algorithm. Adv Sci Lett. 2016;22(10):3120–3.

    Article  Google Scholar 

  14. Patro S, Sahu K. Normalization: a preprocessing stage. IARJSET. 2015. p. 20–22.

  15. Cox V. Translating statistics to make decisions. 2017.

  16. Kumar S, Chong I. Correlation analysis to identify the effective data in machine learning: prediction of depressive disorder and emotion states. Int J Environ Res Public Health. 2018;15(12):2907.

    Article  Google Scholar 

  17. Hamlich M, Bellatreche L, Mondal A, Ordonez C. Smart applications and data analysis. Cham: Springer; 2020. p. 165–77.

    Book  Google Scholar 

  18. Abdoh SF, Abo Rizka M, Maghraby FA. Cervical cancer diagnosis using random forest classifier with SMOTE and feature reduction techniques. IEEE Access. 2018;6:59475–85.

    Article  Google Scholar 

  19. Dimitrakopoulos GN, Vrahatis AG, Plagianakos V, Sgarbas K. Pathway analysis using XGBoost classification in Biomedical Data. In: Proc. of the 10th hellenic conference on artificial intelligence. Association for computing machinery, New York, NY, USA, Article 46, 2018. p. 1–6.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh

    Laboni Akter

  2. Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh

    Ferdib-Al-Islam, Md. Milon Islam & Md. Rezwanul Haque

  3. Research Chair of Pervasive and Mobile Computing, Information Systems Department, College of Computer and Information Sciences, King Saud University,, Riyadh, 11543, Saudi Arabia

    Mabrook S. Al-Rakhami

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  1. Laboni Akter
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  2. Ferdib-Al-Islam
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  4. Mabrook S. Al-Rakhami
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  5. Md. Rezwanul Haque
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Correspondence to Mabrook S. Al-Rakhami.

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This article is part of the topical collection “Advances in Computational Approaches for Artificial Intelligence, Image Processing, IoT and Cloud Applications” guest edited by Bhanu Prakash K N and M. Shivakumar.

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Akter, L., Ferdib-Al-Islam, Islam, M.M. et al. Prediction of Cervical Cancer from Behavior Risk Using Machine Learning Techniques. SN COMPUT. SCI. 2, 177 (2021). https://doi.org/10.1007/s42979-021-00551-6

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  • DOI: https://doi.org/10.1007/s42979-021-00551-6

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