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Ovary Cancer Detection using Decision Tree Classifiers based on Historical Data of Ovary Cancer Patients

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CMBEBIH 2017

Part of the book series: IFMBE Proceedings ((IFMBE,volume 62))

Abstract.

This research implements decision tree classifiers and artificial neural network to predict whether the patient will live with ovary cancer or not. Dataset was obtained from Danish Cancer Register and contains five Input parameters. Dataset contains some missing values and a noticeable improvement in accuracy was detected after removing them. Three features of the original dataset were shown to be the most significant: Mobility of the cancer, Surface of the cancer, and the Consistency of the cancer. The addition of the other two features (Size of the cancer and age of the patient) did not improve the results significantly. It was noticed that the patients with a cystic, but fixed and even cancer have always died from the ovary cancer. In contrast, the patients with uneven, but fixed and solid cancer have always survived the cancer. It is recommended to include more information about either the cancer or the patient to increase the chance of predicting the output of such patients.

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Authors and Affiliations

  1. International Burch University, Sarajevo, Bosnia and Herzegovina

    Ahmed Osmanović, Layla Abdel-Ilah, Adnan Hodžić & Jasmin Kevric

  2. Graz University of Technology, Graz, Austria

    Adnan Fojnica

Authors
  1. Ahmed Osmanović
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  2. Layla Abdel-Ilah
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  3. Adnan Hodžić
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  4. Jasmin Kevric
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  5. Adnan Fojnica
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Corresponding author

Correspondence to Ahmed Osmanović .

Editor information

Editors and Affiliations

  1. Faculty of Engineering and Information Technologies, International Burch University , Sarajevo, Bosnia and Herzegovina

    Almir Badnjevic

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Osmanović, A., Abdel-Ilah, L., Hodžić, A., Kevric, J., Fojnica, A. (2017). Ovary Cancer Detection using Decision Tree Classifiers based on Historical Data of Ovary Cancer Patients. In: Badnjevic, A. (eds) CMBEBIH 2017. IFMBE Proceedings, vol 62. Springer, Singapore. https://doi.org/10.1007/978-981-10-4166-2_77

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  • DOI: https://doi.org/10.1007/978-981-10-4166-2_77

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-4165-5

  • Online ISBN: 978-981-10-4166-2

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