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Predicting Cancer Survivability: A Comparative Study

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Advances in Internet, Data and Web Technologies (EIDWT 2019)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 29))

Abstract

The prediction of cancer survivability in patients remains a challenging task due to its complexity and heterogeneity. Nevertheless, studying cancer survivability has been receiving an increasing attention essentially because of the positive impact it has on patients and physicians. It helps physicians determine the suitable treatment options, gives hope to patients, and improves their psychological state. This paper aims to predict the survival period a patient can live after being diagnosed with cancer disease by surveying the performance of three different regression algorithms. The three regression algorithms used are Decision Tree Regression, Multilayer Perceptron Regression, and Support Vector Regression. The algorithms are trained and tested on nine cancer types selected from the SEER dataset. The prediction models of each regression algorithm are built using cross validation evaluation method and ensemble method. Our experimental results show that Decision Tree Regression outperforms the others in predicting the survival period in all the nine cancer types.

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Notes

  1. 1.

    http://www.seer.cancer.gov.

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Author information

Authors and Affiliations

  1. The University of Jordan, Amman, Jordan

    Ola Abu Elberak, Loai Alnemer & Majdi Sawalha

  2. Higher Colleges of Technology, Fujariah, UAE

    Jamal Alsakran

Authors
  1. Ola Abu Elberak
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  2. Loai Alnemer
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  3. Majdi Sawalha
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  4. Jamal Alsakran
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Corresponding author

Correspondence to Jamal Alsakran .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Technical University of Catalonia, Barcelona, Spain

    Fatos Xhafa

  3. Computer Information Science (CIS), Higher Colleges of Technology, Fujairah Campus, Fujairah, United Arab Emirates

    Zahoor Ali Khan

  4. Faculty of Computer Information Science, Higher Colleges of Technology, Dubai Campus, Dubai, United Arab Emirates

    Hamad Odhabi

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Abu Elberak, O., Alnemer, L., Sawalha, M., Alsakran, J. (2019). Predicting Cancer Survivability: A Comparative Study. In: Barolli, L., Xhafa, F., Khan, Z., Odhabi, H. (eds) Advances in Internet, Data and Web Technologies. EIDWT 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 29. Springer, Cham. https://doi.org/10.1007/978-3-030-12839-5_19

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