Employing various machine learning algorithms to predict mortality after acute myocardial infarction using GUSTO-1 medical data

Sharipov, D.K., Saidov, A.D., Шарипов, Д.К., Саидов, А.Д.

Ҳисоблаш ва амалий математика муаммолари · 2024-yil

Annotatsiya

Now in the world there are a lot of diseases threatening humanity and for these medical treatments and diagnostic methods have also improved both in practical and statistical. This paper discusses one of the most leading diseases, myocardial infarction. The mortality results showed that 18.8% of the patients hospitalized with MI diagnosis had died. Gender results revealed that around 28% of the deceased MI patients were women and 14% of them were men. So, by these statistics there are vital needs emerg ing in the diagnostician and prediction for making disease prevention and mitigation plans. To do that, one of cutting-edge methods is classical Ml prediction algorithms. The experiments and results showed that GUSTO-I trial data was used to train models in different kinds of ML algorithms, in terms of accuracy, the Random Forest algorithm showed slightly better results. And, to evaluate the model different methods are used including cross-validation and metrics are precision, Recall (Sensitivity), F1 Score, Con fusion Matrix but the final accuracy metric was Confusion Matrix. In the end, to predict long term mortality from myocardial infarction, a machine learning model trained and analyzed that for the GUSTO-I cardio data, the slightly effective algorithm is random forest.

Maqola ma’lumotlari
MualliflarSharipov, D.K., Saidov, A.D., Шарипов, Д.К., Саидов, А.Д.
JurnalҲисоблаш ва амалий математика муаммолари
NashriyotТошкент ахборот технологиялари университети қошидаги, Ахборот-коммуникация технологиялари илмий-инновацион, маркази
ISSN2181-8460, 2181-046X
Nashr sanasi2024-10-11
Son4
Betlar135-147
TilIngliz
Ilmiy sohaTexnika
Indeksga qo‘shilgan2026-08-27

Maqolaning asl sahifasi

Kalit so‘zlar

ml, logistic regression, decision trees, random forest, GUSTO-I, myocardial infarction, cross-validation, машинное обучение, логистическая регрессия, дерево решений, случайный лес, GUSTO-I, инфаркт миокарда, кросс-валидация

Ilmiy soha

Texnika

Iqtibos keltirish

APASharipov, D.K., Saidov, A.D., Шарипов, Д.К., & Саидов, А.Д. (2024). Employing various machine learning algorithms to predict mortality after acute myocardial infarction using GUSTO-1 medical data. Ҳисоблаш ва амалий математика муаммолари, (4), 135-147. https://journals.airi.uz/index.php/pvpm/article/view/38
GOST (OAK)Sharipov, D.K. va b. Employing various machine learning algorithms to predict mortality after acute myocardial infarction using GUSTO-1 medical data // Ҳисоблаш ва амалий математика муаммолари. – 2024. – № 4. – P. 135-147.
MLASharipov, D.K., Saidov, A.D., Шарипов, Д.К., and Саидов, А.Д. “Employing various machine learning algorithms to predict mortality after acute myocardial infarction using GUSTO-1 medical data.” Ҳисоблаш ва амалий математика муаммолари, (4), 135-147, 2024. https://journals.airi.uz/index.php/pvpm/article/view/38
ChicagoSharipov, D.K., Saidov, A.D., Шарипов, Д.К., and Саидов, А.Д. “Employing various machine learning algorithms to predict mortality after acute myocardial infarction using GUSTO-1 medical data.” Ҳисоблаш ва амалий математика муаммолари, (4), 135-147 (2024). https://journals.airi.uz/index.php/pvpm/article/view/38
HarvardSharipov, D.K., Saidov, A.D., Шарипов, Д.К., and Саидов, А.Д (2024) ‘Employing various machine learning algorithms to predict mortality after acute myocardial infarction using GUSTO-1 medical data’, Ҳисоблаш ва амалий математика муаммолари, (4), 135-147. https://journals.airi.uz/index.php/pvpm/article/view/38
BibTeX
@article{sharipovdk2024,
  title = {Employing various machine learning algorithms to predict mortality after acute myocardial infarction using GUSTO-1 medical data},
  author = {Sharipov, D.K. and Saidov, A.D. and Шарипов, Д.К. and Саидов, А.Д.},
  journal = {Ҳисоблаш ва амалий математика муаммолари},
  year = {2024},
  number = {4},
  pages = {135-147},
  url = {https://journals.airi.uz/index.php/pvpm/article/view/38}
}

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