Classification of Epileptic Seizure Dataset Using Different Machine Learning Algorithms and PCA Feature Reduction Technique

Abstract

Epileptic seizures are currently one of the leading reasons for morbidity and mortality in the world. With the rise of epileptic seizures around the world and their effect on people's lives, it's more important than ever to get an accurate and timely diagnosis. These days, machine learning techniques are utilized to forecast or diagnose various life-threatening diseases such as epilepsy, cancer, diabetes, heart disease, thyroid, and so on. Early detection and treatment of diseases such as epilepsy will save a person's life. The fundamental goal of this work is to find the best classification algorithm for epileptic seizures by applying the Principal Components Analysis (PCA) feature reduction technique in the dataset. In this paper, we applied K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Decision Tree (DT) algorithms by using the PCA feature reduction technique in the dataset to predict epilepsy, and the performance of classifiers are analyzed with using PCA and without using the PCA technique. The models used in this analysis have various degrees of accuracy. This study indicates that the used model can accurately predict epilepsy. Our findings indicate that using PCA feature reduction in the dataset, the random forest classifier (RF) with 97 % accuracy and low computational times (training and testing time) produces the best results. Also, the K-Nearest Neighbors (KNN) and Random Forest Classifier (RF) with 99 % accuracy without using PCA feature reduction in the dataset shows the best result compared to other machine learning techniques.

Keywords

• Epileptic Seizures
• K-Nearest Neighbors (KNN)
• Machine Learning (ML)
• Principal Components Analysis (PCA)
• Random Forest (RF)

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