INTRODUCTION
HF, cardiovascular disease, impaired fasting glucose, prediabetes, normoglycemia, Diabetes, Heart failure, prediabetes or diabetes, HF death, diabetes
CARDIOVASCULAR DISEASE, PREDIABETES, IMPAIRED GLUCOSE TOLERANCE, PREDIABETES, DIABETES, HEART FAILURE, DISEASES, DIABETES
Heart failure (HF) has grown significantly in importance as a public health issue and as a population health danger in recent decades.Diabetes is one of an independent risk factor for HF, which has been confirmed in many studies.Prediabetes, which includes impaired fasting glucose and impaired glucose tolerance (IGT), is the metabolic condition in between diabetes and normoglycemia.Currently, artificial intelligence is widely used in the medical field, and machine learning (ML) is increasingly used in medical treatment, prediction and diagnosis of diseases.Early and accurate detection of the risk of HF is essential to reducing the related adverse health effects given the connection between diabetes or prediabetes and HF. There are few predictive models for the risk of pre‐diabetes or diabetic HF constructed using ML algorithms. This study used a ML approach to develop a predictive model for HF based on data from the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2018 to identify at‐risk populations. The development of a prediction model based on the risk of developing HF in patients with prediabetes or diabetes could help to accurately identify individuals at risk for HF death in patients with diabetes, providing an opportunity to personalize preventive therapy and potentially reduce the burden of cardiovascular disease in patients with prediabetes or diabetes.