DEVELOPMENT OF ARTIFICIAL INTELLIGENCE MODELS FOR SHORT-TERM PREDICTION OF HIGH-RISK HYPERTENSIVE CRISES IN PRIMARY CARE SETTINGS

Authors

  • Inomov Kamoliddin Mamasoli o’g’li Biznes va fanlar universiteti, Tibbiyot fakulteti, Tibbiyot kafedrasi
  • Xoshimov Muslimbek Toxirjon o’g’li Biznes va fanlar universiteti, Tibbiyot fakulteti, Umumiy kasbiy fanlar kafedrasi
  • Karimjonov Jaloliddin Abdusattor o’g’li Biznes va fanlar universiteti, Tibbiyot fakulteti, Umumiy kasbiy fanlar kafedrasi
  • Soliyev Muhammadqodir Abdugaffor o’g’li Biznes va fanlar universiteti, Tibbiyot fakulteti, Tibbiyot kafedrasi
  • Nosirov Temurbek Isroiljon o’g’li Biznes va fanlar universiteti, Tibbiyot fakulteti, Tibbiyot kafedrasi
  • Khamrayev Abdurahmon Umar o’g’li Biznes va fanlar universiteti, Tibbiyot fakulteti, Tibbiyot kafedrasi

Keywords:

Artificial Intelligence, Hypertensive Crisis, Machine Learning, Primary Care, Risk Prediction, Cardiology.

Abstract

Hypertensive crisis remains one of the leading causes of cardiovascular complications. Traditional risk scales focus on long-term consequences and fail to predict acute events in the primary setting. Objective: To develop and validate a Machine Learning model for short-term (7-day) prediction of hypertensive crises. Methods: A retrospective cohort study was conducted using electronic medical records of 50 patients with essential hypertension. Three different algorithms (Logistic Regression, Random Forest, XGBoost) were compared. Hemodynamic variability, biochemical markers, and medication adherence were taken as the main factors.

References

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Published

2026-01-21

Issue

Vol. 3 No. 1 (2026): UNIVERSAL INTERNATIONAL SCIENTIFIC JOURNAL

Section

Articles

How to Cite

DEVELOPMENT OF ARTIFICIAL INTELLIGENCE MODELS FOR SHORT-TERM PREDICTION OF HIGH-RISK HYPERTENSIVE CRISES IN PRIMARY CARE SETTINGS. (2026). Universal International Scientific Journal, 3(1), 139-144. https://universaljurnal.uz/index.php/jurnal/article/view/3883