Cardiovascular Disease Prediction using Machine Learning Methods
Year: 2024
Type: conference-paper
Abstract: Computer-aided automatic diagnosis systems are used to speed up diagnosis and treatment and to help make the best decision. In this study, eight different classification methods are considered for the detection of cardiovascular disease and their performances are analyzed separately. Firstly, the preprocessing step is carried out and the original data set is normalized with the Standard Scaler. Then, the original data is used splitting into train and test with proportions of 70% and 30%, a classification step is applied by using eight different algorithms including K-Nearest Neighbor (K-NN), Bernoulli Naive Bayes (BNB), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Decision Tree (DT), AdaBoost, Logistic Regression (LR) and Random Forest (RF). The success of the classifiers is observed for four different performance criteria. Accordingly, the RF classifier exhibits the best performance with %99 accuracy. In addition, it obtains precision, kappa, Mean Square Error-MSE values as 0.994, 0.980, 0.01, respectively.
Author: Tuğba Palabaş
Institution: Zonguldak Bülent Ecevit University
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4406729757