DISCUSSION
myocardial infarction, chest pain, prediabetes, diabetic, prediabetes or diabetes, diabetes
MYOCARDIAL INFARCTION, CARDIOVASCULAR DISEASE, PREDIABETES, REGRESSION, EVENTS, CORONARY HEART DISEASE, DIABETES
In this study, age, PIR, myocardial infarction condition, coronary heart disease condition, chest pain condition, and glucose‐lowering medication use were screened as predictors of HF by univariate and multivariate logistic regression analysis.Based on these predictors, five ML algorithms were developed and validated using five ML models to predict the risk of HF in patients with prediabetes or diabetes. By comparing the degree of calibration, discrimination, and clinical utility of the five ML models, the results showed that the RF model had the best predictive ability for HF in patients with prediabetes or diabetes.The PIR ranked highest among the five predictors in the RF model. According to our data results, the risk of HF was negatively correlated with the PIR. The effect of income on HF has been confirmed in many studies, with low‐income groups being more likely to develop HF or have a higher mortality rate from HF than higher‐income groups, possibly because low‐income groups tend to receive more limited medical resources.Age was the second most important predictor. The expected 5‐year mortality from HF in diabetic people younger than 75 years of age increased dramatically with age, according to the findings of a prospective research involving 283 patients with chronic HF.As a cardiovascular disease with an acute onset, a large proportion of myocardial infarction survivors will eventually develop HF.In our study, people with diabetes who took glucose‐lowering drugs had a significantly lower risk of HF compared to patients who did not take glucose‐lowering drugs. Several studies have demonstrated that patients with HF benefit greatly from glucose‐lowering medications such dapagliflozin, canagliflozin, and empagliflozin by reducing mortality and improving outcomes.RF algorithm is an accurate, easy to interpret and computationally efficient ML algorithm defined by a collection of learning methods for classification, regression and other tasks.There are few studies on using ML methods to predict the risk of HF in people with diabetes or prediabetes. To our knowledge, this is the first study to develop a predictive model for HF risk assessment in people with diabetes or prediabetes using ML algorithms using data from the NHANES database with easy‐to‐use clinical and laboratory data, and the RF model we developed can be used by clinicians to make accurate clinical decisions.Our research does, however, have some drawbacks. First off, because the NHANES database is cross‐sectional in character, the exact order of events is still unknown. Second, the subjects with diabetes or prediabetes we extracted were obtained through patient home interviews. However, the specific subtypes of diabetes or prediabetes are not yet known. Therefore, further studies are needed to investigate the effects of various different types of diabetes or prediabetes on HF. Third, despite the fact that the research first split the entire data set into a training and validation set and used the validation set for internal validation, it still lacks external validation to evaluate the validity of model. Additionally, because our study is based on research done in a U.S. population, a multicenter study with participants from various nations and areas would be necessary to ensure the accuracy of the results. Fourth, the data we used were all extracted from NHANES, and many of the variables were obtained through interviews. This may have impacted the correctness of our data due to potential participant memory biases, which would have impacted the objectivity of the findings.