Comparative Study on Heart Disease Prediction Using Feature Selection Techniques on Classification Algorithms

Table 4

Parameter settings for classifier.

Classification algorithm	Basic parameters
Random forest (RF)	n_estimators = 100, random_state = 0, n_jobs = -1
Support vector machine (SVM)	kernel = “linear”, degree = 2
Decision tree (DT)	Criterion = “gini”, splitter = “best”, random_state = 0
K-nearest neighbor (KNN)	n_neighbors = 4, weights = “distance”, n_jobs = -1
Logistic regression (LR)	C = 1, penalty = “l1/l2”, solver = “liblinear”, random_state = 7
Gaussian naive Bayes	var_smoothing = 0.05