Development and validation of a cardiovascular risk prediction model for Sri Lankans using machine learning
SLCVD score is the first and only CV risk prediction model specific for Sri Lankans. It predicts the 10-year risk of developing a hard CVD in Sri Lankans. SLCVD score was more effective in predicting Sri Lankans at high CV risk than WHO risk charts.
Authors
- Udaya K. Ranawaka (ORCID: https://orcid.org/0000-0002-4050-062X)
- Nei Kato (ORCID: https://orcid.org/0000-0001-8769-302X)
- Tolani Weerasinghe
- Chamila Mettananda (ORCID: https://orcid.org/0000-0002-3328-1553)
- Sujeewani Kurukulasooriya (ORCID: https://orcid.org/0000-0001-8765-9366)
- Janaka de Silva
- Ayodya Ranasinghe
- Anuradhani Kasturiratne (ORCID: https://orcid.org/0000-0001-5260-2394)
- Arunasalam Pathmeswaran (ORCID: https://orcid.org/0000-0003-4065-2639)
- Prasanna S. Haddela (ORCID: https://orcid.org/0000-0002-6969-8772)
- Rajitha Wickramasinghe
- Rashmi Sammandapperuma
- Isuru Sanjeewa
- Tinul Benthota Arachchi
- Avishka Wijesooriya
- Chiranjaya Chandrasena
- Maheeka Solangaarachchige
- Isuru Elpitiya
Institutions
- University of Kelaniya (LK)
- Sri Lanka Institute of Information Technology (LK)
- National Center for Global Health and Medicine (JP)
Publication Details
- Journal
- PLoS ONE
- Published
- 2024-10-22
- DOI
- https://doi.org/10.1371/journal.pone.0309843
- Citations
- 5
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 3.54
Funders
- University of Kelaniya