Comparative Analysis of Quantum Models in the Prognosis of Cardiovascular Diseases

Адилова, Ф.Т., Давронов, Р.Р.

Рақамли технологияларнинг назарий ва амалий масалалари · 2025-yil

Annotatsiya

This paper analyzes two approaches to the prediction of cardiovascular diseases, which have a binary classification problem based on the same Cleveland benchmark and aim to identify the advantages of quantum computing over traditional classifiers. This is a hybrid quantum random forest (HQNN, HQRF) for predicting the development of coronary heart disease in the early stages and an explainable prediction of cardiovascular diseases by the ensemble-quantum learning of Bagging-QSVC. The first approach uses various feature selection methods, which usually require a lot of computational effort, while the HQRF model and the earlier HQNN model are high-speed algorithms due to the nature of quantum computing. Numerical results show that HQRF is more suitable for small datasets, while HQNN is better suited for large datasets. In the second approach, the Bagging-QSVC model uses a quantum support vector classifier as a basic classifier. The results of the model are explained through the importance of the contribution of each individual feature using the Shaply (SHAP) algorithm. Comparative studies of other quantum classifiers on the Cleveland benchmark show the superiority of Bagging-QSVC with an accuracy of 90.16%. It follows from this that quantum machine learning classifiers are more effective than classical machine learning classifiers in predicting diseases of the cardiovascular system.

Maqola ma’lumotlari
MualliflarАдилова, Ф.Т., Давронов, Р.Р.
JurnalРақамли технологияларнинг назарий ва амалий масалалари
NashriyotТАТУ Самарқанд филиали
ISSN2181-3086, 2181-3094
Nashr sanasi2025-07-25
Jild8
Son2
Betlar7-16
TilIngliz
Ilmiy sohaTexnika
DOI10.62132/ijdt.v8i2.259
Indeksga qo‘shilgan2026-08-26

DOI: 10.62132/ijdt.v8i2.259 · Maqolaning asl sahifasi

Kalit so‘zlar

машинное обучение, ансамблевое обучение, квантовое машинное обучение, объяснимое машинное обучение, прогнозирование сердечных заболеваний, machine learning, ensemble learning, quantum machine learning, explainable machine learning, heart disease prediction

Ilmiy soha

Texnika

Iqtibos keltirish

APAАдилова, Ф.Т., & Давронов, Р.Р. (2025). Comparative Analysis of Quantum Models in the Prognosis of Cardiovascular Diseases. Рақамли технологияларнинг назарий ва амалий масалалари, 8(2), 7-16. https://doi.org/10.62132/ijdt.v8i2.259
GOST (OAK)Адилова, Ф.Т., Давронов, Р.Р. Comparative Analysis of Quantum Models in the Prognosis of Cardiovascular Diseases // Рақамли технологияларнинг назарий ва амалий масалалари. – 2025. – T. 8, № 2. – P. 7-16. – DOI: 10.62132/ijdt.v8i2.259.
MLAАдилова, Ф.Т., and Давронов, Р.Р. “Comparative Analysis of Quantum Models in the Prognosis of Cardiovascular Diseases.” Рақамли технологияларнинг назарий ва амалий масалалари, 8(2), 7-16, 2025. https://doi.org/10.62132/ijdt.v8i2.259
ChicagoАдилова, Ф.Т., and Давронов, Р.Р. “Comparative Analysis of Quantum Models in the Prognosis of Cardiovascular Diseases.” Рақамли технологияларнинг назарий ва амалий масалалари, 8(2), 7-16 (2025). https://doi.org/10.62132/ijdt.v8i2.259
HarvardАдилова, Ф.Т., and Давронов, Р.Р (2025) ‘Comparative Analysis of Quantum Models in the Prognosis of Cardiovascular Diseases’, Рақамли технологияларнинг назарий ва амалий масалалари, 8(2), 7-16. https://doi.org/10.62132/ijdt.v8i2.259
BibTeX
@article{article2025,
  title = {Comparative Analysis of Quantum Models in the Prognosis of Cardiovascular Diseases},
  author = {Адилова, Ф.Т. and Давронов, Р.Р.},
  journal = {Рақамли технологияларнинг назарий ва амалий масалалари},
  year = {2025},
  volume = {8},
  number = {2},
  pages = {7-16},
  doi = {10.62132/ijdt.v8i2.259},
  url = {https://doi.org/10.62132/ijdt.v8i2.259}
}

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