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Primary prevention cardiovascular disease risk prediction model for contemporary Chinese (1°P-CARDIAC): Model derivation and validation using a hybrid statistical and machine-learning approach.

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Title: Primary prevention cardiovascular disease risk prediction model for contemporary Chinese (1°P-CARDIAC): Model derivation and validation using a hybrid statistical and machine-learning approach.
Authors: Zhou Y; School of Computing and Data Science, The University of Hong Kong, Hong Kong Special Administration Region, China.; Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong Science and Technology Park, Hong Kong Special Administration Region, China., Lin CJ; School of Nursing, The University of Hong Kong, Hong Kong Special Administration Region, China., Yu Q; Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong Science and Technology Park, Hong Kong Special Administration Region, China.; Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, The University of Hong Kong, Hong Kong Special Administration Region, China., Blais JE; Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, The University of Hong Kong, Hong Kong Special Administration Region, China., Wan EYF; Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, The University of Hong Kong, Hong Kong Special Administration Region, China.; Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Queen Mary Hospital, Hong Kong Special Administration Region, China.; Advanced Data Analytics for Medical Science (ADAMS) Limited, Hong Kong Special Administration Region, China., Wong E; Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administration Region, China., Tan K; Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administration Region, China., Siu DC; Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administration Region, China., Yiu KH; Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administration Region, China., Chan EWY; Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong Science and Technology Park, Hong Kong Special Administration Region, China.; Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, The University of Hong Kong, Hong Kong Special Administration Region, China., Yu D; School of Nursing, The University of Hong Kong, Hong Kong Special Administration Region, China., Wong W; Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Queen Mary Hospital, Hong Kong Special Administration Region, China., Lam TW; School of Computing and Data Science, The University of Hong Kong, Hong Kong Special Administration Region, China., Wong ICK; Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong Science and Technology Park, Hong Kong Special Administration Region, China.; Advanced Data Analytics for Medical Science (ADAMS) Limited, Hong Kong Special Administration Region, China.; Aston Pharmacy School, Aston University, Birmingham, United Kingdom., Luo R; School of Computing and Data Science, The University of Hong Kong, Hong Kong Special Administration Region, China.; Advanced Data Analytics for Medical Science (ADAMS) Limited, Hong Kong Special Administration Region, China., Chui CSL; Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong Science and Technology Park, Hong Kong Special Administration Region, China.; School of Nursing, The University of Hong Kong, Hong Kong Special Administration Region, China.; Advanced Data Analytics for Medical Science (ADAMS) Limited, Hong Kong Special Administration Region, China.
Source: PloS one [PLoS One] 2025 Jul 28; Vol. 20 (7), pp. e0322419. Date of Electronic Publication: 2025 Jul 28 (Print Publication: 2025).
Publication Type: Journal Article; Validation Study
Journal Info: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
Database: MEDLINE Ultimate
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ISSN:1932-6203
DOI:10.1371/journal.pone.0322419