Advanced machine learning techniques for cardiovascular disease early detection and diagnosis

Journal of Big Data

Table 12 Comparative results on the Dataset using ML

Classifier	Accuracy	Precision	Recall	F1
XGBoost	0.8297	0.8980	0.8049	0.8489
AdaBoost	0.8659	0.9262	0.8415	0.8818
LinearDiscriminant	0.8696	0.9156	0.8598	0.8868
LightGBM	0.8732	0.9057	0.8780	0.8916
GradientBoosting	0.8768	0.9276	0.8598	0.8924
Catboost	0.8804	0.9226	0.8720	0.8966
ExtraTree	0.8804	0.9281	0.8659	0.8959
KNeighbors	0.8841	0.9074	0.8963	0.9018
SVM	0.8841	0.8976	0.9085	0.9030
LogisticRegression	0.8841	0.9231	0.8780	0.9000
RandomForest	0.8877	0.9236	0.8841	0.9034
Catboost_tuned	0.9094	0.9317	0.9146	0.9231