Prediction of Coronary Artery Disease Risk Using Genetic and Phenotypic Variables.
| Autor: | SNG, Letitia M. F., SHARMA, Reevanshi, BAGOT, Sam, BAUER, Denis C., TWINE, Natalie A. |
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| Předmět: |
CAROTID intima-media thickness
SINGLE nucleotide polymorphisms CONFERENCES & conventions MACHINE learning RANDOM forest algorithms RISK assessment DIAGNOSTIC imaging CORONARY artery disease GENOTYPES DESCRIPTIVE statistics PREDICTION models RECEIVER operating characteristic curves SENSITIVITY & specificity (Statistics) PHENOTYPES ALGORITHMS DISEASE risk factors |
| Zdroj: | Medinfo; 2023, Vol. 310, p1021-1025, 5p |
| Abstrakt: | 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. [ABSTRACT FROM AUTHOR] |
| Databáze: | Complementary Index |
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