  Aim: to evaluate the ability of the nonlinear 24-HRV as a predictor of MI
using Machine Learning Methods: The sample was composed of 218 patients divided
into two groups (Healthy, n=128; MI n=90). The sample dataset is part of the
Telemetric and Holter Electrocardiogram Warehouse (THEW) database, from the
University of Rochester Medical Center. We used the most common ML algorithms
for accuracy comparison with a setting of 10-fold cross-validation (briefly,
Linear Regression, Linear Discriminant Analysis, k-Nearest Neighbour, Random
Forest, Supporting Vector Machine, Na\"ive Bayes, C 5.0 and Stochastic Gradient
Boosting). Results: The main findings of this study show that the combination
of SD1nu + SD2nu has greater predictive power for MI in comparison to other HRV
indexes. Conclusion: The ML model using nonlinear HRV indexes showed to be more
effective than the linear domain, evidenced through the application of ML,
represented by a good precision of the Stochastic Gradient Boosting model.
  Keywords: heart rate variability, machine learning, nonlinear domain,
cardiovascular disease
