📄 Sciences Methods and Technologies
International Journal (SciMeTech)

Volume 2 · Issue 1 · 2026
ISSN: 3085-5284
Diabetes Prediction using Quantum Artificial Intelligence
Bissam Elaziz, Nadia Hachoumi, Charaf Eddine AIT ZAOUIAT, Yassin LAAZIZ
Pages 91–98 · LabITC, ENSA of Tangier, Abdelmalek Essaadi University, Morocco · Cadi Ayyad University, UCA, Higher School of Technology of Essaouira, LaRTID Laboratory, Marrakech, Morocco · Polydisciplinary Faculty of Sidi Bennour, Chouaib Doukkali University, Morocco
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Abstract
The global epidemic of diabetes, driven by lifestyle and genetic factors and leading to severe complications, necessitates the use of Artificial Intelligence to predict its progression for enabling early, life-saving interventions. Quantum Artificial Intelligence combines principles of Quantum Computing and Artificial Intelligence, offering potential improvements in efficiency and accuracy for complex data-driven tasks. In this paper, we explore the use of Quantum Artificial Intelligence algorithms for binary classification, focusing on clustering methods for diabetes prediction. Specifically, we compare the performance of classical KMeans clustering with a quantum-enhanced version based on a Variational Quantum Classifier model. The Quantum KMeans algorithm includes quantum properties such as superposition and entanglement to enhance cluster formation and classification performance. Using the Pima Indian diabetes dataset, preprocessed with standard scaling, we evaluated and compared the results of classical KMeans and the quantum special model. Our experiments demonstrate that the quantum-enhanced KMeans achieves competitive performance compared to the classical approach, suggesting that Quantum Artificial Intelligence techniques offers more effective and scalable clustering in diabetes binary classification tasks. These findings highlight the complementary role of quantum computing in advancing classical machine learning methods for medical diagnosis.
Keywords: Diabetes Prediction, Quantum Artificial Intelligence, Variational Quantum Classifier, Quantum KMeans, Quantum computing

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SciMeTech · Volume 2, Issue 1 · 2026 · ISSN 3085-5284