A Method for Improving Prediction of Human Heart Disease Using Machine Learning Algorithms

Table 2

Best values for hyper parameter tuning to improve classifiers accuracy.

Algorithm	Best Hyperparameter Values
SVM	Kernel: sigmoid, C:0.5
CART	Max features = “auto,” random state = 123, min samples split = 20, min samples leaf = 11
AB	N estimators = 50, learning rate = 0.05
LDA	Shrinkage = “auto,” solver = “lsqr”
GBM	N estimators:250
RF	Criterion = “gini,” n jobs = −1, min samples leaf = 2, min samples split = 5, n estimators = 15, random state = 123
ET	N jobs = −1, min samples leaf = 1, n estimators = 15, random state = 123, criterion = “gini,” Min samples split = 6
XBoost	objective = “reg: linear,” colsample bytree = 0.3, learning rate = 0.1, max depth = 15, alpha = 5, n estimators = 123