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Data Mining Approach to Classify Cases of Lung Cancer

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Book cover Trends and Applications in Information Systems and Technologies (WorldCIST 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1365))

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Abstract

According to the World Cancer Research Fund, a leading authority on cancer prevention research, lung cancer is the most commonly occurring cancer in men and the third most commonly occurring cancer in women, with the 5-year relative survival percentage being significantly low. Smoking is the major risk factor for lung cancer and the symptoms associated with it include cough, fatigue, shortness of breath, chest pain, weight loss, and loss of appetite. In an attempt to build a model capable of identifying individuals with lung cancer, this study aims to build a data mining classification model to predict whether or not a patient has lung cancer based on crucial features such as the above mentioned symptoms. Through the CRISP-DM methodology and the RapidMiner software, different models were built, using different scenarios, algorithms, sampling methods, and data approaches. The best data mining model achieved an accuracy of 93%, a sensitivity of 96%, a specificity of 90% and a precision of 91%, using the Artificial Neural Network algorithm.

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Acknowledgments

This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.

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Authors and Affiliations

  1. Department of Information, University of Minho, Braga, Portugal

    Eduarda Vieira

  2. Algoritmi Research Center, University of Minho, Campus of Gualtar, Braga, Portugal

    Diana Ferreira, Cristiana Neto, António Abelha & José Machado

Authors
  1. Eduarda Vieira
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  2. Diana Ferreira
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  3. Cristiana Neto
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  4. António Abelha
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  5. José Machado
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Corresponding author

Correspondence to José Machado .

Editor information

Editors and Affiliations

  1. ISEG, University of Lisbon, Lisbon, Portugal

    Álvaro Rocha

  2. College of Engineering, The Ohio State University, Columbus, OH, USA

    Hojjat Adeli

  3. Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania

    Gintautas Dzemyda

  4. DCT, Universidade Portucalense, Porto, Portugal

    Fernando Moreira

  5. Department of Information Sciences, University of Sheffield, Lisbon, Portugal

    Ana Maria Ramalho Correia

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Vieira, E., Ferreira, D., Neto, C., Abelha, A., Machado, J. (2021). Data Mining Approach to Classify Cases of Lung Cancer. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Ramalho Correia, A.M. (eds) Trends and Applications in Information Systems and Technologies. WorldCIST 2021. Advances in Intelligent Systems and Computing, vol 1365. Springer, Cham. https://doi.org/10.1007/978-3-030-72657-7_49

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