Fadheela Hussain*, Wael Elmedany, Mustafa Hamad
Cardiovascular disease diagnostic is a significan’t matter and an important topic in machine learning. Researchers used several data mining methods to support healthcare professionals in the diagnosis of diseases. Concerning this objective, many algorithms have been proposed in earlier years. This research has proposed four different supervised machine learning classifiers, Artificial Neural Networks (ANN), Decision Tree (DT), Nave Bayes (NB) and Support Vector Machine (SVM), classifiers using WEKA to implement the technical assessment of this study. Two different heart disease datasets have been used in this experiment. The two datasets are, Cleveland and Hungarian clinic foundation heart dis ease, which are available at UCI machine learning repository. It has been found that ANN outperformed the three other classifiers, giving the best rate of accuracy and having the highest number of instances correctly classified. Moreover, Nave Bayes (NB) had attained the highest competitive output rate with respect to ROC measurement.
Published Date: 2025-01-22; Received Date: 2024-07-30
