Центральноазиатский журнал академических исследований 3-jild 5-son (2025) · 20–29-betlar

PREDICTION OF CARDIOVASCULAR DISEASE THROUGH LINEAR REGRESSION USING ARTIFICIAL INTELLIGENCE.

Nadirova, Yulduz, Bobosharipov, Feruz

DOI: 10.5281/zenodo.15350098 · Manbada o'qish → · PDF (manba serverida)

Annotatsiya

The surge in cardiovascular diseases (CVDs) has become a global challenge with a steadily climbing trend of cardiovascular deaths from 12.1 million in 1990 to 18.6 million in 2019 [1, 2]. Risk prediction, a primary strategy in addressing this worldwide problem, has brought significant benefits to some developed countries through the improvement of the effectiveness of life intervention and reduction of economic burden [3, 4]. Therefore, risk prediction has been expected as an efficient way to achieve World Health Organization (WHO) goals for reducing CVD-related mortality by 25% by 2025, and some classic CVD prediction models (e.g., the Framingham [5] and SCORE [6], referred to as traditional models [T-Ms] in this study) has been incorporated into clinical guidelines by the European Society of Cardiology (ESC) and the American College of Cardiology/American Heart Association (ACC/AHA) [7, 8].

We conducted this systematic review using the CHARMS checklist. This review has been registered in the international prospective register of systematic reviews (PROSPERO), with the registration number CRD42021271789, where all updates of the review will also be recorded

Metadata manbasi: jurnal OAI-PMH arxivi · Sindex to'liq matnni saqlamaydi, manbaga havola beradi.