Machine Learning for Gastric Cancer Detection: A Logistic Regression Approach

Abraham Pouliakis, Periklis Foukas, Konstantinos Triantafyllou, Niki Margari, Efrossyni Karakitsou, Vasileia Damaskou, Nektarios Koufopoulos, Tsakiraki Zoi, Martha Nifora, Alina-Roxani Gouloumi, Ioannis G. Panayiotides and Michael Tzivras
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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

International Journal of Reliable and Quality E-Healthcare (IJRQEH), 2020, vol. 9, issue 2, 48-58

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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jrqeh0:v:9:y:2020:i:2:p:48-58

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