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Advanced Computing and Intelligent Technologies pp 199–209Cite as

A Framework for Lung Cancer Detection Using Machine Learning

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Part of the Lecture Notes in Electrical Engineering book series (LNEE,volume 914)

Abstract

Cancer is a lethal disease that has been ruling the world for the death of millions of people every year. It causes abnormal and uncontrollable growth of cells and destroys the body tissues and so similarly, in lung cancer, cells in the lungs grow uncontrollably. So, the detection of lung cancer is a must as we also know that the earlier the detection, the survival rate of the victim also increases accordingly. In many cases, cancer is detected lately and the chances to save the patient’s life also diminishes. The figure of persons diagnosed with lung cancer is directly proportionate to the figure of chain smokers and other hazardous addictions. The prediction of cancer and its prognosis has been a tedious task for doctors and medical workers since cancer’s discovery. Among the various applied technologies, supervised machine learning is a very relevant option in which a particular model is trained to work on the complex datasets, containing different features, and then the prediction is made accordingly. There will be the usage of different techniques and algorithms (like K-nearest neighbors, decision trees, naïve Bayes, kernelized support vector machines, and logistic regression) to work on the labeled datasets. Adding up, the model basically focuses on two foci: cancer susceptibility and model accuracy.

Keywords

  • Cancer detection
  • Supervised machine learning
  • Labeled datasets
  • KNN
  • Logistic regression
  • Naïve Bayes
  • Support vector machine

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References

  1. Cruz, J.A., Wishart, D.S.: Applications of machine learning in cancer prediction and prognosis. Cancer Informatics. (2006). https://doi.org/10.1177/117693510600200030

    CrossRef  Google Scholar 

  2. Radhika, P.R., Nair, R.A.S., Venna. G.: A comparative study of lung cancer detection using machine learning algorithms. In: 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), pp. 1–4 (2019). https://doi.org/10.1109/ICECCT.2019.8869001

  3. Tekade, R., Rajeswari, K.: Lung cancer detection and classification using deep learning. In: 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA) (2018), pp. 1–5. https://doi.org/10.1109/ICCUBEA.2018.8697352 (Rado, Suhi, H. (eds.) Academic, New York, pp. 271–350) (1963)

  4. Kadir, T., Gleeson, F.: Lung cancer prediction using machine learning and advanced imaging techniques. Transl. Lung Cancer Res. 7(3), 304 (2018)

    CrossRef  Google Scholar 

  5. Faisal, M.I. et al.: An evaluation of machine learning classifiers and ensembles for early stage prediction of lung cancer. In: 2018 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology (ICEEST). IEEE (2018)

    Google Scholar 

  6. Günaydin, Ö., Günay, M., Şengel, Ö.: Comparison of lung cancer detection algorithms. Sci. Meeting Electr. Electron. Biomed. Eng. Computer Sci. (EBBT) 2019, 1–4 (2019). https://doi.org/10.1109/EBBT.2019.8741826

    CrossRef  Google Scholar 

  7. Abdullah, D.M., Ahmed, S.W. (2021): A review of most recent lung cancer detection techniques using machine learning. In: International Journal of Science and Business, IJSAB International, vol. 5(3), pp. 159–173.M. Young, The Technical Writer’s Handbook. University Science, Mill Valley, CA (1989)

    Google Scholar 

  8. Mridha, K. et al.: Web based brain tumor detection using neural network. In: 2021 IEEE 6th International Conference on Computing, Communication and Automation (ICCCA), pp. 137–143 (2021). https://doi.org/10.1109/ICCCA52192.2021.9666248

  9. Alam, J., Alam, S., Hossan, A.: Multi-stage lung cancer detection and prediction using multi-class SVM classifie. In: 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2). IEEE (2018)

    Google Scholar 

  10. Patra, R.: Prediction of lung cancer using machine learning classifier. In: International Conference on Computing Science, Communication and Security. Springer, Singapore (2020)

    Google Scholar 

  11. Ismail, M.B.S.: Lung cancer detection and classification using machine learning algorithm. Turkish J. Computer Math. Educ. (TURCOMAT) 12(13), 7048–705 (2021)

    Google Scholar 

  12. Wu, Q, Zhao, W.: Small-cell lung cancer detection using a supervised machine learning algorithm. In: 2017 International Symposium on Computer Science and Intelligent Controls (ISCSIC). IEEE (2017)

    Google Scholar 

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Authors and Affiliations

  1. School of Engineering and Technology, Sharda University, Greater Noida, India

    Aakash Nakarmi, Anil Kumar Sagar, Seerat Musharaf & Hadya Jahangir

Authors
  1. Aakash Nakarmi
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  2. Anil Kumar Sagar
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  3. Seerat Musharaf
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  4. Hadya Jahangir
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Corresponding author

Correspondence to Aakash Nakarmi .

Editor information

Editors and Affiliations

  1. Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India

    Rabindra Nath Shaw

  2. Regional Campus Manipur, Indira Gandhi National Tribal University, Imphal, Manipur, India

    Sanjoy Das

  3. Department of Computer Science, University of Milan, Milan, Italy

    Vincenzo Piuri

  4. Department of Information Engineering and Mathematics, University of Siena, Siena, Italy

    Monica Bianchini

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Nakarmi, A., Sagar, A.K., Musharaf, S., Jahangir, H. (2022). A Framework for Lung Cancer Detection Using Machine Learning. In: Shaw, R.N., Das, S., Piuri, V., Bianchini, M. (eds) Advanced Computing and Intelligent Technologies. Lecture Notes in Electrical Engineering, vol 914. Springer, Singapore. https://doi.org/10.1007/978-981-19-2980-9_17

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  • DOI: https://doi.org/10.1007/978-981-19-2980-9_17

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