Established machine learning models to predict readmission for elderly patients with ischemic heart disease

WHAT'S NEW?Few studies evaluated the readmissions for elderly patients with ischemic heart disease.Therefore, we developed a predictive model based on machine learning.The results showed that the categorical boosting model had better predictive performance and good calibration in identifying 30-day and 1-year readmissions for elderly patients with ischemic heart disease.Meanwhile, we found that the age-adjusted Charlson comorbidity index, brain natriuretic peptide, heart failure, cholesterol, free thyroxine, thymidine kinase 1, osmotic pressure and red blood cell distribution width-standard deviation were common risk factors for readmission at different time.