PREDICTION OF CARDIOVASCULAR DISEASE THROUGH LINEAR REGRESSION USING ARTIFICIAL INTELLIGENCE.
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2025
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| author | Nadirova, Yulduz Bobosharipov, Feruz |
| author_facet | Nadirova, Yulduz Bobosharipov, Feruz |
| contents | <p><span lang="UZ-CYR">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]. </span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15350098 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | PREDICTION OF CARDIOVASCULAR DISEASE THROUGH LINEAR REGRESSION USING ARTIFICIAL INTELLIGENCE. Nadirova, Yulduz Bobosharipov, Feruz <p><span lang="UZ-CYR">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]. </span></p> |
| title | PREDICTION OF CARDIOVASCULAR DISEASE THROUGH LINEAR REGRESSION USING ARTIFICIAL INTELLIGENCE. |
| url | https://doi.org/10.5281/zenodo.15350098 |