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Machine Learning for Gastric Cancer Detection: A Logistic Regression Approach

Author

Listed:
  • Abraham Pouliakis

    (2nd Department of Pathology, National and Kapodistrian University of Athens, Athens, Greece)

  • Periklis Foukas

    (2nd Department of Pathology, National and Kapodistrian University of Athens, Athens, Greece)

  • Konstantinos Triantafyllou

    (Hepatogastrenterology Unit 2nd Department of Medicine National and Kapodistrian University of Athens, Athens, Greece)

  • Niki Margari

    (Independent Researcher, Greece)

  • Efrossyni Karakitsou

    (Department of Biology, University of Barcelona, Barcelona, Spain)

  • Vasileia Damaskou

    (2nd Department of Pathology, National and Kapodistrian University of Athens, Athens, Greece)

  • Nektarios Koufopoulos

    (2nd Department of Pathology, National and Kapodistrian University of Athens, Athens, Greece)

  • Tsakiraki Zoi

    (2nd Department of Pathology, National and Kapodistrian University of Athens, Athens, Greece)

  • Martha Nifora

    (2nd Department of Pathology, National and Kapodistrian University of Athens, Athens, Greece)

  • Alina-Roxani Gouloumi

    (2nd Department of Pathology, National and Kapodistrian University of Athens, Athens, Greece)

  • Ioannis G. Panayiotides

    (2nd Department of Pathology, National and Kapodistrian University of Athens, Athens, Greece)

  • Michael Tzivras

    (Emeritus Professor, National and Kapodistrian University of Athens, Athens, Greece)

Abstract

The objective of this study is the investigation of the potential value of a logistic regression model for the classification of gastric cytological data. The model was based on the morphological features of cell nuclei. The aim was the discrimination of benign from malignant nuclei and subsequently patients. Cytological images of gastric smears were analyzed by an image analysis system capable to extract cell nuclear features. Measurements from 50% of the patients were selected as a training set for model creation, while the measurements from the remaining patients were used as test set to validate the results. Furthermore, a model for the classification of individual patients, based on the classification of their cell nuclei has been developed. This approach set gave a correct classification at the level of 90% on the training and test sets on the nuclear level. Concluding the application of morphometric feature selection in combination with logistic regression may offer useful and complementary information about the potential of malignancy of gastric nuclei and patient cases.

Suggested Citation

  • Abraham Pouliakis & Periklis Foukas & Konstantinos Triantafyllou & Niki Margari & Efrossyni Karakitsou & Vasileia Damaskou & Nektarios Koufopoulos & Tsakiraki Zoi & Martha Nifora & Alina-Roxani Goulou, 2020. "Machine Learning for Gastric Cancer Detection: A Logistic Regression Approach," International Journal of Reliable and Quality E-Healthcare (IJRQEH), IGI Global, vol. 9(2), pages 48-58, April.
    • Handle: RePEc:igg:jrqeh0:v:9:y:2020:i:2:p:48-58
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    Cited by:

    1. Hirad Baradaran Rezaei & Alireza Amjadian & Mohammad Vahid Sebt & Reza Askari & Abolfazl Gharaei, 2023. "An ensemble method of the machine learning to prognosticate the gastric cancer," Annals of Operations Research, Springer, vol. 328(1), pages 151-192, September.

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