Machine learning identifies factors related to obesity, dyslipidemia, and inflammation as predictors of noncalcified coronary burden in patients with psoriasis, according to study results published in the Journal of the American Academy of Dermatology.
Managing risk for cardiovascular disease (CVD) and cardiac events in patients with psoriasis requires accurate assessment of risk factors and disease predictors. Machine learning algorithms improve the predictive power of clinical and imaging data and provide greater prognostic capacity for CVD risk stratification. Researchers aimed to apply machine learning to determine the top predictors of noncalcified coronary burden in patients with psoriasis using random forest algorithms.
