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

Machine Learning;Quantitative Methods;Machine Learning