Diabetes type 2: poincaré data preprocessing for quantum machine learning

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dc.contributor.authorSierra-Sosa, Daniel
dc.contributor.authorArcila-Moreno, Juan D.
dc.contributor.authorGarcía-Zapirain, Begoña
dc.contributor.authorElmaghraby, Adel Said
dc.date.accessioned2025-08-12T10:18:27Z
dc.date.available2025-08-12T10:18:27Z
dc.date.issued2021-02-05
dc.date.updated2025-08-12T10:18:27Z
dc.description.abstractQuantum Machine Learning (QML) techniques have been recently attracting massive interest. However reported applications usually employ synthetic or well-known datasets. One of these techniques based on using a hybrid approach combining quantum and classic devices is the Variational Quantum Classifier (VQC), which development seems promising.Albeit being largely studied, VQC implementations for "real-world" datasets are still challenging on Noisy Intermediate Scale Quantum devices (NISQ). In this paper we propose a preprocessing pipeline based on Stokes parameters for data mapping. This pipeline enhances the prediction rates when applying VQC techniques, improving the feasibility of solving classification problems using NISQ devices. By including feature selection techniques and geometrical transformations, enhanced quantum state preparation is achieved.Also, a representation based on the Stokes parameters in the Poincare Sphere is possible for visualizing the data.Our results showthat by using the proposed techniques we improve the classification score for the incidence of acute comorbid diseases in Type 2 Diabetes Mellitus patients. We used the implemented version of VQC available on IBM s framework Qiskit, and obtained with two and three qubits an accuracy of 70% and 72% respectively.en
dc.description.sponsorshipThis project was partially funded by eVIDA Research group IT-905-16 from Basque Government.en
dc.identifier.citationSierra-Sosa, D., Arcila-Moreno, J. D., Garcia-Zapirain, B., & Elmaghraby, A. (2021). Diabetes type 2: poincaré data preprocessing for quantum machine learning. Computers, Materials and Continua, 67(2), 1849-1861. https://doi.org/10.32604/CMC.2021.013196
dc.identifier.doi10.32604/CMC.2021.013196
dc.identifier.eissn1546-2226
dc.identifier.issn1546-2218
dc.identifier.urihttps://hdl.handle.net/20.500.14454/3356
dc.language.isoeng
dc.publisherTech Science Press
dc.rights© 2021 The Author(s)
dc.subject.otherData Preprocessing
dc.subject.otherPoincare Sphere
dc.subject.otherQuantum Machine Learning
dc.subject.otherStokes Parameters
dc.titleDiabetes type 2: poincaré data preprocessing for quantum machine learningen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.endPage1861
oaire.citation.issue2
oaire.citation.startPage1849
oaire.citation.titleComputers, Materials and Continua
oaire.citation.volume67
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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