Diabetes Type 2: Poincar�Data Preprocessing for Quantum Machine Learning
Open accessAbstract
Quantum 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... | Find, read and cite all the research you need on Tech Science Press
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D.Sierra-Sosa et al. [23] utilized the VQC model to predict diabetes with acute disease and reached a maximum accuracy of 72% on the Diabetes dataset with three variables.
“D.Sierra-Sosa et al. [23] utilized the VQC model to predict diabetes with acute disease and reached a maximum accuracy of 72% on the Diabetes dataset with three variables.”
From Variational Quantum Classifier for Binary Classification: Real vs Synthetic Dataset (2022) · cites this paper as [23]
The VQC cost function is calculated using iterative device measurements, which helps mitigate errors by integrating noisy data in the optimization computations.
“The cost function is calculated using iterative device measurements, which helps to mitigate errors by integrating noisy data in the optimization computations [21], [23].”
From Variational Quantum Classifier for Binary Classification: Real vs Synthetic Dataset (2022) · cites this paper as [23]
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References 44 · Cited by 16
- Authors
- Sierra-Sosa, D., D. Arcila-Moreno, J., Garcia-Zapirain, B., & Elmaghraby, A.
- Corresponding author
- Sierra-Sosa, Daniel
- Venue
- Computers, Materials & Continua
- Publisher
- Tech Science Press
- Published
- 2021
- Volume
- 67
- Issue
- 2
- Pages
- 1849-1861
- ISSN
- 1546-2218 (ISSN-L)
- 1546-2226
- OpenAlex
- W3129494485
- Cited by
- 16
- References
- 44
- Citations by year
- 2026: 5 · 2025: 3 · 2024: 4 · 2022: 2 · 2021: 2
- FWCI
- 0.71
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Computers, materials & continua/Computers, materials & continua (Print) (publishedVersion, cc-by)
- DeustoTeka (submittedVersion, cc-by-nc-nd)
- Funders
- Eusko Jaurlaritza (IT-905-16)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- type 2 diabetes, data preprocessing, quantum machine learning, hybrid quantum-classical algorithms, biomedical data
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