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Predict Survival of Patients with Lung Cancer Using an Ensemble Feature Selection Algorithm and Classification Methods in Data Mining

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  • Mahdis Dezfuly
  • Hedieh Sajedi

Abstract

This research proposes an efficient model for predicting the survival rate of patients affected by lung cancer. The researchers collected data from four feature categories (population, recognition, treatment, and result) of cancer patients based on the importance of the survival of patients with lung cancer. Analyses of the predicted survival rates of the patients indicate that, among the classification algorithms, Decision Tree C5.0 results the highest accuracy. The models were created using algorithms based on the level of death risk in five stages: six months, nine months, one year, two years, and five years. In this paper, we proposed a mechanism for feature selection. Our mechanism combines the results of some feature section algorithm. The results illustrate that out mechanism outperform other feature selection algorithms. After applying the proposed mechanism for feature selection, the accuracy of the C5.0 algorithm was equivalent to 97.93%.

Suggested Citation

  • Mahdis Dezfuly & Hedieh Sajedi, 2015. "Predict Survival of Patients with Lung Cancer Using an Ensemble Feature Selection Algorithm and Classification Methods in Data Mining," Journal of Information, Conscientia Beam, vol. 1(1), pages 1-11.
    • Handle: RePEc:pkp:joinfo:v:1:y:2015:i:1:p:1-11:id:2505
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    File URL: https://archive.conscientiabeam.com/index.php/104/article/view/2505/3857
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    Cited by:

    1. Mozhgan Safe & Hossein Mahjub & Javad Faradmal, 2017. "A Comparative Study for Modelling the Survival of Breast Cancer Patients in the West of Iran," Global Journal of Health Science, Canadian Center of Science and Education, vol. 9(2), pages 215-215, February.

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