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

Yıl 2021, Cilt: 4 Sayı: 2, 47 - 60, 31.12.2021

Shamriz Nahzat , Mete Yağanoğlu

Öz

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.

Anahtar Kelimeler

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

Kaynakça

• Fisher, R., Acevedo, C., Arzimanoglou, A., Bogacz, A., Cross, J., Elger, C., et al. (2014). ILAE official report: a practical clinical definition of epilepsy, Epilepsia, 55,4, 475-482.
• Ramgopal, S., Thome-Souza, S., Jackson, M., & Kadish, N. E., Sánchez Fernández, I., Klehm, J., Bosl, W., Reinsberger, C., Schachter, S., & Loddenkemper, T. (2014). Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy. Epilepsy & behavior: E&B, 37,291–307.
• Lehnertz, K., Mormann, F., Kreuz, T., Andrzejak, R. G., Rieke, C., David, P., & Elger, C. E. (2003). Seizure prediction by nonlinear EEG analysis. IEEE engineering in medicine and biology magazine: the quarterly magazine of the Engineering in Medicine & Biology Society, 22,1,57–63.
• Nandy, A., Alahe, M. A., Nasim Uddin, S. M., Alam, S., Nahid, A. A., & Awal, M. A. (2019). Feature Extraction and Classification of EEG Signals for Seizure Detection. 2019 International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST).
• Almustafa, K. M. (2020). Classification of epileptic seizure dataset using different machine learning algorithms. Informatics in Medicine Unlocked, 21, 100444.
• Usman, S. M., Latif, S., & Beg, A. (2019). Principal components analysis for seizures prediction using wavelet transform. International Journal of Advanced and Applied Sciences, 6, 3, 50–55.
• Hamad, A., Houssein, E. H., Hassanien, A. E., & Fahmy, A. A. (2017). A Hybrid EEG Signals Classification Approach Based on Grey Wolf Optimizer Enhanced SVMs for Epileptic Detection. Proceedings of the International Conference on Advanced Intelligent Systems and Informatics, 108–117.
• Sharmila, A., & Geethanjali, P. (2016). DWT Based Detection of Epileptic Seizure from EEG Signals Using Naive Bayes and k-NN Classifiers. IEEE Access, 4, 7716–7727.
• Swami, P., Gandhi, T. K., Panigrahi, B. K., Tripathi, M., & Anand, S. (2016). A novel robust diagnostic model to detect seizures in electroencephalography. Expert Systems with Applications, 56, 116–130.
• Andrzejak, R. G., Lehnertz, K., Mormann, F., Rieke, C., David, P., & Elger, C. E. (2001). Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Physical Review E, 64, 6.
• Soni, M., & Varma, S. (2020), Diabetes Prediction using Machine Learning Techniques, International Journal of Engineering Research & Technology (IJERT), 9, 9.
• Ippolito, P. P. (2019). Feature Extraction Techniques - Towards Data Science. Retrieved, from https://towardsdatascience.com/feature-extraction-techniques-d619b56e31be, (Date of access: December 27, 2020).
• Qiu, J., Wang, H., Lu, J., Zhang, B., & Du, K. L. (2012). Neural Network Implementations for PCA and ItsExtensions. ISRN Artificial Intelligence, 2012, 1–19.
• Ghosh-Dastidar, S., Adeli, H., & Dadmehr, N. (2008). Principal Component Analysis-Enhanced Cosine Radial Basis Function Neural Network for Robust Epilepsy and Seizure Detection. IEEE Transactions on Biomedical Engineering, 55(2), 512–518.
• Yağanoğlu, M., & Köse, C. (2018). Real-Time Detection of Important Sounds with a Wearable Vibration Based Device for Hearing-Impaired People. Electronics, 7, 4, 50.
• Rodrigues, J. D. C., Filho, P. P. R., Peixoto, E., N, A. K., & de Albuquerque, V. H. C. (2019). Classification of EEG signals to detect alcoholism using machine learning techniques. Pattern Recognition Letters, 125, 140–149.
• Mitchell, T. M. (1997). Does Machine Learning Really Work?. AI Magazine, 18, 3, 11..
• Freund, Y., Schapire, R.E., 1996. Experiments with a new boosting algorithm. In: Machine Learning. Proceedings of the Thirteenth International Conference. 148–156.
• Breiman, L. (1996). Bagging predictors. Machine Learning, 24, 2, 123–140.
• Gislason, P. O., Benediktsson, J. A., & Sveinsson, J. R. (2006). Random Forests for land cover classification. Pattern Recognition Letters, 27, 4, 294–300.
• Fawagreh, K., Gaber, M. M., & Elyan, E. (2014). Random forests: from early developments to recent advancements. Systems Science & Control Engineering, 2, 1, 602–609.
• Walczak, S., & Cerpa, N. (2003). Artificial Neural Networks. Encyclopedia of Physical Science and Technology, 631–645.
• Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1, 1, 81–106.
• Geetha, A., & Nasira, G. M. (2014). Data mining for meteorological applications: Decision trees for modeling rainfall prediction. 2014 IEEE International Conference on Computational Intelligence and Computing Research.