Assessment of Myocardial Infarction Patient Survival using Machine Learning Methods

Пекось, О.А.

Рақамли технологияларнинг назарий ва амалий масалалари · 2025-yil

Annotatsiya

High mortality in the acute and post-hospital periods of myocardial infarction underscores the relevance of accurately stratifying patient risk and assessing survival outcomes. This study compares two complementary approaches: the Cox regression model, including time-dependent covariates, and an ensemble random forest method for binary classification and survival analysis. It is shown that accounting for the dynamics of clinical and laboratory indicators (blood pressure, heart rate, necrosis markers, etc.) improves both the discrimination and calibration of prognostic models. Variants of the instantaneous hazard equation, partial likelihood formulation, and formulas for estimating survival and cumulative hazard are presented. Illustrative examples based on a mixed dataset of real and synthetic data demonstrate the advantages of the combined approach in the presence of censoring and heterogeneous risk profiles. The results confirm the feasibility of integrating such models into the clinical workflow to personalize treatment and rehabilitation strategies after myocardial infarction.

Maqola ma’lumotlari
MualliflarПекось, О.А.
JurnalРақамли технологияларнинг назарий ва амалий масалалари
NashriyotТАТУ Самарқанд филиали
ISSN2181-3086, 2181-3094
Nashr sanasi2025-10-27
Jild8
Son4
Betlar48-57
TilIngliz
Ilmiy sohaTexnika
DOI10.62132/ijdt.v8i4.302
Indeksga qo‘shilgan2026-08-26

DOI: 10.62132/ijdt.v8i4.302 · Maqolaning asl sahifasi

Kalit so‘zlar

машинное обучение, анализ выживаемости, прогнозирование летальности, модель Кокса, случайный лес выживания, machine learning, survival analysis, mortality prediction, Cox model, survival random forest

Ilmiy soha

Texnika

Iqtibos keltirish

APAПекось, О.А. (2025). Assessment of Myocardial Infarction Patient Survival using Machine Learning Methods. Рақамли технологияларнинг назарий ва амалий масалалари, 8(4), 48-57. https://doi.org/10.62132/ijdt.v8i4.302
GOST (OAK)Пекось, О.А. Assessment of Myocardial Infarction Patient Survival using Machine Learning Methods // Рақамли технологияларнинг назарий ва амалий масалалари. – 2025. – T. 8, № 4. – P. 48-57. – DOI: 10.62132/ijdt.v8i4.302.
MLAПекось, О.А. “Assessment of Myocardial Infarction Patient Survival using Machine Learning Methods.” Рақамли технологияларнинг назарий ва амалий масалалари, 8(4), 48-57, 2025. https://doi.org/10.62132/ijdt.v8i4.302
ChicagoПекось, О.А. “Assessment of Myocardial Infarction Patient Survival using Machine Learning Methods.” Рақамли технологияларнинг назарий ва амалий масалалари, 8(4), 48-57 (2025). https://doi.org/10.62132/ijdt.v8i4.302
HarvardПекось, О.А (2025) ‘Assessment of Myocardial Infarction Patient Survival using Machine Learning Methods’, Рақамли технологияларнинг назарий ва амалий масалалари, 8(4), 48-57. https://doi.org/10.62132/ijdt.v8i4.302
BibTeX
@article{article2025,
  title = {Assessment of Myocardial Infarction Patient Survival using Machine Learning Methods},
  author = {Пекось, О.А.},
  journal = {Рақамли технологияларнинг назарий ва амалий масалалари},
  year = {2025},
  volume = {8},
  number = {4},
  pages = {48-57},
  doi = {10.62132/ijdt.v8i4.302},
  url = {https://doi.org/10.62132/ijdt.v8i4.302}
}

Boshqa maqola uchun iqtibos tayyorlash

Рақамли технологияларнинг назарий ва амалий масалалари jurnalidan boshqa maqolalar

Рақамли технологияларнинг назарий ва амалий масалалари — barcha maqolalar · 2025-yil arxivi

Texnika sohasidagi boshqa maqolalar

Texnika sohasidagi OAK jurnallari