{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T10:56:31Z","timestamp":1774436191346,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684567","type":"print"},{"value":"9781643684574","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T00:00:00Z","timestamp":1706140800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,25]]},"abstract":"<jats:p>Coronary artery disease (CAD) has the highest disease burden worldwide. To manage this burden, predictive models are required to screen patients for preventative treatment. A range of variables have been explored for their capacity to predict disease, including phenotypic (age, sex, BMI and smoking status), medical imaging (carotid artery thickness) and genotypic. We use a machine learning models and the UK Biobank cohort to measure the prediction capacity of these 3 variable categories, both in combination and isolation. We demonstrate that phenotypic variables from the Framingham risk score have the best prediction capacity, although a combination of phenotypic, medical imaging and genotypic variables deliver the most specific models. Furthermore, we demonstrate that Variant Spark, a random forest based GWAS platform, performs effective feature selection for SNP-based genotype variables, identifying 115 significantly associated SNPs to the CAD phenotype.<\/jats:p>","DOI":"10.3233\/shti231119","type":"book-chapter","created":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T10:26:23Z","timestamp":1706178383000},"source":"Crossref","is-referenced-by-count":1,"title":["Prediction of Coronary Artery Disease Risk Using Genetic and Phenotypic Variables"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0217-9927","authenticated-orcid":false,"given":"Letitia M.F.","family":"Sng","sequence":"first","affiliation":[{"name":"Australian e-Health Research Centre, CSIRO, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Reevanshi","family":"Sharma","sequence":"additional","affiliation":[{"name":"Australian e-Health Research Centre, CSIRO, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sam","family":"Bagot","sequence":"additional","affiliation":[{"name":"Department of Biotechnology and Biomolecular Sciences, UNSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8033-9810","authenticated-orcid":false,"given":"Denis C.","family":"Bauer","sequence":"additional","affiliation":[{"name":"Australian e-Health Research Centre, CSIRO, Australia"},{"name":"Department of Applied BioSciences, Macquarie University, Australia"},{"name":"Department of Biomedical Science, Macquarie University, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2051-8275","authenticated-orcid":false,"given":"Natalie A.","family":"Twine","sequence":"additional","affiliation":[{"name":"Australian e-Health Research Centre, CSIRO, Australia"},{"name":"Department of Applied BioSciences, Macquarie University, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2023 \u2014 The Future Is Accessible"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI231119","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T10:26:31Z","timestamp":1706178391000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI231119"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,25]]},"ISBN":["9781643684567","9781643684574"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti231119","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,25]]}}}