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Heart disease remains the leading cause of death in the United States.Compared with risk assessment guidelines that require manual calculation ofscores, machine learning-based prediction for disease outcomes such asmortality can be utilized to save time and improve prediction accuracy. Thisstudy built and evaluated various machine learning models to predict one-yearmortality in patients diagnosed with acute myocardial infarction or postmyocardial infarction syndrome in the MIMIC-III database. The results of thebest performing shallow prediction models were compared to a deep feedforwardneural network (Deep FNN) with back propagation. We included a cohort of 5436admissions. Six datasets were developed and compared. The models applyingLogistic Model Trees (LMT) and Simple Logistic algorithms to the combineddataset resulted in the highest prediction accuracy at 85.12% and the highestAUC at .901. In addition, other factors were observed to have an impact onoutcomes as well.

Building Computational Models to Predict One-Year Mortality in ICU Patients with Acute Myocardial Infarction and Post Myocardial Infarction Syndrome