Autors: Gancheva, V. S., Tsvetkov V.
Title: Quantum Machine Learning for Enhanced Cardiovascular Disease Risk Prediction †
Keywords: artificial intelligence, machine learning, medical data, neural networks, quantum computing, support vector machines

Abstract: Quantum computing has emerged as a powerful tool for solving complex problems in various fields. Personalized medicine, tailoring medical treatment to patients based on their genetic and health data, is one area where predictive analytics can be useful. This work explores the application of quantum algorithms for predictive analytics, specifically in the context of predicting outcomes of cardiovascular disease based on patient data. This research is focused on the quantum-based predictive models for the case study of cardiovascular disease. The models are based on Quantum Support Vector Machines, Quantum Neural Networks, and Variational Quantum Eigensolver algorithms. The software implementation is based on the Python programming language, including an integrated quantum algorithm. A dataset of cardiovascular disease from an online platform is used to train and evaluate the models.

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Issue

Engineering Proceedings, vol. 150, 2026, Switzerland, https://doi.org/10.3390/engproc2026150039

Вид: статия в списание, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus