Prediction of patients with heart failure after myocardial infarction

Heart failure is one of the most common causes of death for all different racial groups all over the world. It is noticed that a high proportion of patients diagnosed with heart failure (HF) within a short period of time after suffering from myocardial infarction (MI). This study is designed to translate existing real world data to real world evidence by exploring associations between historical comorbidities and heart failure diseases. Machine learning technologies were applied to predict whether patients with myocardial infarction would develop heart failure within a specific time period, and to remind patients how to strengthen personal self-care to avoid the transition towards heart failure or postpone the occurrence of the preceding events. In this study, patients with heart failure after myocardial infarction were divided into two groups according to a median age of 71 years old, and corresponding prediction models were constructed for two different age groups respectively. Three different machine learning technologies, namely logistic regression, random forest, and XGBoost were used to construct prediction models and a 5-fold cross-validation was applied to evaluate prediction accuracy and stability of prediction models. The results of our proposed method reveal that if a prediction model was constructed without age stratification, the constructed prediction model provided inferior performance compared to stratified groups by employing identical features. The analytical results from three different machine learning techniques consistently supported that the prediction models of myocardial infarction resulted in accelerated transition towards heart failure within a specific interval should be constructed by stratifying age groups first, and then training the corresponding data for better system performance.