Classification of epileptic seizure dataset using different machine learning algorithms

Seizure associated with abnormal brain activities caused by epileptic disorder is widely typical and has many symptoms, such as loss of awareness and unusual behavior as well as confusion. In this paper, a classification of the Epileptic Seizure dataset was done using different classifiers. It was s...

Full description

Saved in:
Bibliographic Details
Published in:Informatics in medicine unlocked 2020, Vol.21, p.100444, Article 100444
Main Author: Almustafa, Khaled Mohamad
Format: Article
Language:English
Subjects:
Classification
Decision tree
Epileptic seizure
Feature extraction
K-nearest neighbor
Random forest
Sensitivity analysis
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Seizure associated with abnormal brain activities caused by epileptic disorder is widely typical and has many symptoms, such as loss of awareness and unusual behavior as well as confusion. In this paper, a classification of the Epileptic Seizure dataset was done using different classifiers. It was shown that the Random Forest classifier outperformed K- Nearest Neighbor (K-NN), Naïve Bayes, Logistic Regression, Decision Tree (D.T.), Random Tree, J48, Stochastic Gradient Descent (S.G.D.) classifiers with 97.08% Accuracy, ROC = 0.996, and RMSE = 0.1527. Sensitivity analysis for some of these classifiers was performed to study the performance of the classifier to classify the Epileptic Seizure dataset with respect to some changes in their parameters. Then a prediction of the dataset using feature selection based on attributes variance was also performed.
ISSN:2352-9148
2352-9148
DOI:10.1016/j.imu.2020.100444