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Ensemble Machine Learning Models for Breast Cancer Identification

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Artificial Intelligence Applications and Innovations. AIAI 2023 IFIP WG 12.5 International Workshops (AIAI 2023)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 677))

Abstract

The advances in the Machine Learning (ML) domain, from pattern recognition to computational statistical learning, have increased its utility for breast cancer as well by contributing to the screening strategy of diverse risk factors with complex relationships and personalized early prediction. In this work, we focused on Ensemble ML models after using the synthetic minority oversampling technique (SMOTE) with 10-fold cross-validation. Models were compared in terms of precision, accuracy, recall and area under the curve (AUC). After the experimental evaluation, the model that prevailed over the others was the Rotation Forest achieving accuracy, precision and recall equal to 82% and an AUC of 87.4%.

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Acknowledgements

This research was funded by the European Union and Greece (Partnership Agreement for the Development Framework 2014–2020) under the Regional Operational Programme Ionian Islands 2014–2020, project title: “Indirect costs for project “Smart digital applications and tools for the effective promotion and enhancement of the Ionian Islands bio-diversity” ”, project number: 5034557.

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Patras, Patras, Greece

    Elias Dritsas

  2. Department of Informatics and Computer Engineering, University of West Attica, Egaleo, Greece

    Maria Trigka & Phivos Mylonas

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  1. Elias Dritsas
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  2. Maria Trigka
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  3. Phivos Mylonas
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Correspondence to Elias Dritsas .

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Editors and Affiliations

  1. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  2. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  3. Democritus University of Thrace, Xanthi, Greece

    Antonios Papaleonidas

  4. Hellenic Telecom Organization OTE, Athens, Greece

    Ioannis Chochliouros

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Dritsas, E., Trigka, M., Mylonas, P. (2023). Ensemble Machine Learning Models for Breast Cancer Identification. In: Maglogiannis, I., Iliadis, L., Papaleonidas, A., Chochliouros, I. (eds) Artificial Intelligence Applications and Innovations. AIAI 2023 IFIP WG 12.5 International Workshops. AIAI 2023. IFIP Advances in Information and Communication Technology, vol 677. Springer, Cham. https://doi.org/10.1007/978-3-031-34171-7_24

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  • DOI: https://doi.org/10.1007/978-3-031-34171-7_24

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