Skip to main content
SpringerLink
Log in

Machine Learning for Green Smart Health Toward Improving Cancer Data Feature Awareness

  • Chapter
  • First Online:
Computational Intelligence Techniques for Green Smart Cities

Part of the book series: Green Energy and Technology ((GREEN))

  • 356 Accesses

Abstract

Radiation therapy and chemotherapy may be considered life-saving treatments for cancer patients. Though this treatment is not always successful, it left very higher effects on our environment. Drugs used in cancer treatment stop the growth and division of cells; released into the environment, they can affect the ecosystem through altered fertility and increased genetic defects. For a safer greener world undoubtedly, we can rely on machine learning technology to diagnose cancer in early stages. Hence, we may call that choosing the right influencing feature may reduce morbidity and green technology for early cancer diagnosis. Cervical cancer is an excellent example for such a study, as well as impacting individuals, families, and the environment. Cervical cancer presents almost no symptoms at the early stages of development of this condition. Because multi-factors may be involved, this demands a lot of research and analysis to identify causative or linked features. Choosing the right influencing feature is a challenging field in data science due to the presence and complexity of multi-dimensional data. The researchers have applied and optimized an ensemble learning algorithm as it is the best model for multi-modal medical data when relatively high dimensionality is present. The main objective of this study was to minimize the dependency on data pre-processing techniques, while analyzing the data (filling/ignoring missing values with the statistical method). Also, increasing such awareness of feature selection will immensely impact the environment (e.g., chemotherapy-free, less radiation therapy). Main factors were studied and validated using root mean square error (RSME) and mean absolute error (MAE). The classification accuracy for features was obtained by tenfold cross-validation and test (where 66% is training data and 34% test data).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Coombs, N.J., et al.: Environmental and social benefits of the targeted intraoperative radiotherapy for breast cancer: data from UK TARGIT-A trial centres and two UK NHS hospitals offering TARGIT IORT. BMJ Open 6(5), e010703 (2016).https://doi.org/10.1136/bmjopen-2015-010703

  2. Saslow, D., et al.: American Cancer Society, American Society for Colposcopy and Cervical Pathology, and American Society for Clinical Pathology screening guidelines for the prevention and early detection of cervical cancer. CA Cancer J Clin 62(3), 147–172 (2012)

    Google Scholar 

  3. Krawczyk, B., Schaefer, G.: Dealing with the difficult learning situation. Neural Netw. Appl. Electr. Eng. 1(1), 12–15 (2012)

    Google Scholar 

  4. Hasan, M.R., Bakar, N.A.A., Siraj, F., Sainin, M.S., Hasan, M.S.: Single decision tree classifiers’ accuracy on medical data. In: Proceedings of 5th International Conference on Computings and Informatics, ICOCI 2015, no. 188, pp. 671–676 (2015a)

    Google Scholar 

  5. Hasan, M.R., Siraj, F., Sainin, M.S.: Improving ensemble decision tree performance using Adaboost and Bagging. AIP Conf. Proc. 1691, 1–7 (2015b)

    Google Scholar 

  6. Wu, G., Shen, D., Sabuncu, M.R.: Machine Learning, and Medical Imaging. Elsevier Inc. (2016)

    Google Scholar 

  7. Hasan, M.R., Golamhosseini, H., Sarkar, N.I., Safiuzzaman, S.M.: Intrinsic motivated cervical cancer screening intervention framework. Humanit. Technol. Conf., 506–509 (2017a)

    Google Scholar 

  8. Tay, W., Chui, C., Ong, S., Ng, A.C.: Expert systems with applications ensemble-based regression analysis of multimodal medical data for osteopenia diagnosis. Expert Syst. Appl. 40(2), 811–819 (2013)

    Article  Google Scholar 

  9. Arbyn, M., Weiderpass, E., Bruni, L., Sanjosé, S., Saraiya, M., Ferlay, J., Bray, F.: Estimates of incidence and mortality of cervical cancer in 2018: a worldwide analysis. Lancet Global Health 8(2), e191–e203. ISSN: 2214-109X (2019)

    Google Scholar 

  10. Dittman, D.J., Khoshgoftaar, T.M., Napolitano, A.: Selecting the appropriate ensemble learning approach for balanced bioinformatics data. Int. Florida Artif. Intell. Res. Soc., 329–334 (2015)

    Google Scholar 

  11. Blagus, R., Lusa, L.: Boosting for high-dimensional two-class prediction. BMC Bioinform. 16(1), 1–17 (2015)

    Article  Google Scholar 

  12. Ojha, V.K., Jackowski, K., Abraham, A., Snášel, V.: Dimensionality reduction, and function approximation of poly (lactic-co-glycolic acid) micro-and nanoparticle dissolution rate. Int. J. Nanomed. 10, 1119 (2015)

    Article  CAS  Google Scholar 

  13. Nanni, L., Lumini, A., Brahnam, S.: A classifier ensemble approach for the missing feature problem. Artif. Intell. Med. 55(1), 37–50 (2012)

    Article  Google Scholar 

  14. Lee, C.H., Yoon, H.-J.: Medical big data: promise and challenges. Kidney Res. Clin. Pract. 36(1), 3–11 (2017)

    Article  Google Scholar 

  15. Kang, H.: The prevention and handling of the missing data. Korean J. Anesthesiol. 64(5), 402–406 (2013)

    Article  Google Scholar 

  16. Polikar, R., et al.: An ensemble-based data fusion approach for early diagnosis of Alzheimer’s disease. Inf. Fusion 9(1), 83–95 (2008)

    Article  Google Scholar 

  17. Groenwold, R.H.H., Dekkers, O.M.: Missing data: the impact of what is not there. Eur. J. Endocrinol. 183(4), E7–E9 (2020)

    Article  CAS  Google Scholar 

  18. Fletcher, J., Murrell, D.: What is the link between HPV and HIV. Medical News Today, Sussex (2018)

    Google Scholar 

  19. Pietrangelo, N., Ernst, H.: HPV and HIV: What Are the Differences. Healthline media, San Francisco (2018)

    Google Scholar 

  20. Denny, L., Adewole, I., Anorlu, R.: Human papillomavirus prevalence and type distribution in invasive cervical cancer in sub-Saharan Africa. Int. J. Cancer J. Int. du cancer 1(1), 1–7 (2013)

    Google Scholar 

  21. Vyankandondera, V., van de Wijgert.: HIV acquisition is associated with prior high-risk human papillomavirus infection among high-risk women in Rwanda. AIDS 24(1), 2289–2292 (2010)

    Google Scholar 

  22. Schim van der Loeff, M., Nyitray, A., Giuliano, A.: HPV vaccination to prevent HIV infection: time for randomized controlled trials. Sex. Transm. Dis. 38(7), 640–643 (2011)

    Article  Google Scholar 

  23. McCredie, M.R.E., Sharples, K.J., Paul, C.: Natural history of cervical neoplasia and risk of invasive cancer in women with cervical intraepithelial neoplasia. A Retrosp. Cohort Study. Lancet Oncol. 9(5), 425–434 (2008)

    Google Scholar 

  24. Peiperl, L., Coffey, S.: How long can people infected with HIV expect to live. US department of Veteras affair. [Online]. Available: https://www.hiv.va.gov/patient/faqs/life-expectancy-with-HIV.asp. (2017). Accessed 09 Feb 2019

  25. 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

  26. Clifford, G.M., De Vuyst, H., Tenet, V., Plummer, M., Tully, S., Franceschi, S.: Effect of HIV Infection on human papillomavirus types causing invasive cervical cancer in Africa. Epidemiol. Prev. 73(3), 332–339 (2016)

    Google Scholar 

  27. Hasan, M.R., Gholamhosseini, H., Sarkar, N.I.: A new ensemble model for multivariate medical data. In: International Telecommunication Networks And Applications Conference, p. In press. (2017b)

    Google Scholar 

  28. Elhassan, A., Abu-Soud, S., Alghanim, F., Walid, A.S.: ILA4: overcoming missing values in machine learning datasets—an inductive learning approach. J. King Saud Univ. Comput. Inf. Sci. (2021). https://doi.org/10.1016/j.jksuci.2021.02.011

    Article  Google Scholar 

  29. Khan, S.I., Hoque, A.S.M.L.: SICE: an improved missing data imputation technique. J. Big Data 7, 37 (2020). https://doi.org/10.1186/s40537-020-00313-w

    Article  Google Scholar 

  30. Liu, M., Dongre, A.: Proper imputation of missing values in proteomics datasets for differential expression analysis. Briefings Bioinform. 22(3) (2021). https://doi.org/10.1093/bib/bbaa112

  31. Alamoodi, A.H., Zaidan, B.B., Zaidan, A.A., Albahri, O.S., Chen, J., Chyad, M.A., Garfan, S., Aleesa, A.M.: Machine learning-based imputation soft computing approach for large missing scale and non-reference data imputation. Chaos Solitons Fractals 151 (2021)

    Google Scholar 

  32. Fernandes, K., Cardoso, J., Fernandes, J.: Transfer learning with partial observability applied to cervical cancer screening. In: Iberian Conference on Pattern Recognition and Image Analysis. Springer International Publishing (2017)

    Google Scholar 

  33. Moon, H., Ahn, H., Kodell, R.L., Baek, S., Lin, C.-J., Chen, J.J.: Ensemble methods for classification of patients for personalized medicine with high-dimensional data. Artif. Intell. Med. 41(3), 197–207 (2007)

    Article  Google Scholar 

  34. Deeks, S.G., Lewin, S.R., Ross, A.L.: International AIDS Society global scientific strategy: towards an HIV cure 2016. Nat. Med. 22(1), 839–850 (2016)

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We acknowledge Dr. Shariful Hasan from Ampang Puteri Hospital, Malaysia, for his guidance and consultancy during this research. Furthermore, we like to say thanks to Dr. Safiuzzaman, in Chittagong medical college hospitals, who had given to test the system. We also would like to acknowledge Ian Purdon (EIT, New Zealand) for the fruitful discussions.

Author information

Authors and Affiliations

  1. BICC—Cyber Security & IT Solution, Auckland, New Zealand

    Md Rajib Hasan

  2. Eastern Institute of Technology, Napier, New Zealand

    Noor H. S. Alani

  3. SUNY Upstate Medical University, New York, USA

    Rashedul Hasan

Authors
  1. Md Rajib Hasan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Noor H. S. Alani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rashedul Hasan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Md Rajib Hasan .

Editor information

Editors and Affiliations

  1. ENS, Hassan II University, Casablanca, Morocco

    Mohamed Lahby

  2. Hamad bin Khalifa University, Doha, Qatar

    Ala Al-Fuqaha

  3. Sultan Moulay Slimane University, Beni-Mellal, Morocco

    Yassine Maleh

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Hasan, M.R., Alani, N.H.S., Hasan, R. (2022). Machine Learning for Green Smart Health Toward Improving Cancer Data Feature Awareness. In: Lahby, M., Al-Fuqaha, A., Maleh, Y. (eds) Computational Intelligence Techniques for Green Smart Cities. Green Energy and Technology. Springer, Cham. https://doi.org/10.1007/978-3-030-96429-0_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-96429-0_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-96428-3

  • Online ISBN: 978-3-030-96429-0

  • eBook Packages: EnergyEnergy (R0)

Publish with us

Policies and ethics

Search

Navigation