Cardiovascular diseases are one of the leading causes of death worldwide, and their early detection is one of the urgent tasks of modern medicine. In recent years, machine learning algorithms have been widely used to solve this problem. However, in existing studies, a comprehensive comparative analysis of the effectiveness of various algorithms has not been sufficiently covered. This research work provides a comparative analysis of machine learning algorithms used in the early detection of cardiovascular diseases based on approximately 200 scientific sources. The study evaluated the effectiveness of classical models, ensemble methods, deep learning, and hybrid approaches in terms of dataset size, quality, and initial processing stages. The analysis results show that while the classical algorithm is effective in small datasets, ensemble models provide high accuracy and stability in medium-sized data. Such learning models predominate in large and complex datasets, while hybrid approaches yield the highest results. It was also established that the processes of preliminary data processing and feature selection significantly affect the model's efficiency. In conclusion, the most effective approach to early detection of cardiovascular diseases is a complex machine learning sequence that includes preprocessing, trait engineering, and algorithms.
| Mualliflar | Рашидова, Д.Э. |
|---|---|
| Jurnal | Рақамли технологияларнинг назарий ва амалий масалалари |
| Nashriyot | ТАТУ Самарқанд филиали |
| ISSN | 2181-3086, 2181-3094 |
| Nashr sanasi | 2026-08-02 |
| Jild | 9 |
| Son | 3 |
| Betlar | 30-44 |
| Til | Ingliz |
| Ilmiy soha | Texnika |
| DOI | 10.62132/ijdt.v9i3.396 |
| Indeksga qo‘shilgan | 2026-08-26 |
DOI: 10.62132/ijdt.v9i3.396 · Maqolaning asl sahifasi
сердечные заболевания, машинное обучение, ансамблевые модели, глубокое обучение, heart diseases, machine learning, ensemble models, deep learning
| APA | Рашидова, Д.Э. (2026). Comparative analysis of machine learning algorithms in early detection of cardiovascular diseases. Рақамли технологияларнинг назарий ва амалий масалалари, 9(3), 30-44. https://doi.org/10.62132/ijdt.v9i3.396 |
|---|---|
| GOST (OAK) | Рашидова, Д.Э. Comparative analysis of machine learning algorithms in early detection of cardiovascular diseases // Рақамли технологияларнинг назарий ва амалий масалалари. – 2026. – T. 9, № 3. – P. 30-44. – DOI: 10.62132/ijdt.v9i3.396. |
| MLA | Рашидова, Д.Э. “Comparative analysis of machine learning algorithms in early detection of cardiovascular diseases.” Рақамли технологияларнинг назарий ва амалий масалалари, 9(3), 30-44, 2026. https://doi.org/10.62132/ijdt.v9i3.396 |
| Chicago | Рашидова, Д.Э. “Comparative analysis of machine learning algorithms in early detection of cardiovascular diseases.” Рақамли технологияларнинг назарий ва амалий масалалари, 9(3), 30-44 (2026). https://doi.org/10.62132/ijdt.v9i3.396 |
| Harvard | Рашидова, Д.Э (2026) ‘Comparative analysis of machine learning algorithms in early detection of cardiovascular diseases’, Рақамли технологияларнинг назарий ва амалий масалалари, 9(3), 30-44. https://doi.org/10.62132/ijdt.v9i3.396 |
| BibTeX | @article{article2026,
title = {Comparative analysis of machine learning algorithms in early detection of cardiovascular diseases},
author = {Рашидова, Д.Э.},
journal = {Рақамли технологияларнинг назарий ва амалий масалалари},
year = {2026},
volume = {9},
number = {3},
pages = {30-44},
doi = {10.62132/ijdt.v9i3.396},
url = {https://doi.org/10.62132/ijdt.v9i3.396}
} |
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