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In this paper, the average value of prediction time is restricted to 23.4 minutes for a total of 23 subjects. This paper intends to compare the accuracy of three different predictive models, namely \u2013 Logistic Regression, Decision Trees and XGBoost Classifier based on the study of Electroencephalogram (EEG) signals and determine which model has the highest rate of detection of Epileptic Seizure.<\/jats:p>","DOI":"10.3233\/idt-200091","type":"journal-article","created":{"date-parts":[[2021,5,25]],"date-time":"2021-05-25T16:40:59Z","timestamp":1621960859000},"page":"269-279","source":"Crossref","is-referenced-by-count":8,"title":["Comparative investigation of machine learning algorithms for detection of epileptic seizures"],"prefix":"10.1177","volume":"15","author":[{"given":"Akash","family":"Sharma","sequence":"first","affiliation":[]},{"given":"Neeraj","family":"Kumar","sequence":"additional","affiliation":[]},{"given":"Ayush","family":"Kumar","sequence":"additional","affiliation":[]},{"given":"Karan","family":"Dikshit","sequence":"additional","affiliation":[]},{"given":"Kusum","family":"Tharani","sequence":"additional","affiliation":[]},{"given":"Bharat","family":"Singh","sequence":"additional","affiliation":[]}],"member":"179","reference":[{"key":"10.3233\/IDT-200091_ref1","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1007\/s11517-007-0289-4","article-title":"A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysis","volume":"46","author":"De Lucia","year":"2008","journal-title":"Med Biol Eng Comput"},{"key":"10.3233\/IDT-200091_ref2","first-page":"108","article-title":"A hybrid EEG signals classification approach based on grey wolf optimizer enhanced SVMs for epileptic detection","author":"Hamad","year":"2017","journal-title":"International Conference on Advanced Intelligent Systems and Informatics"},{"key":"10.3233\/IDT-200091_ref3","doi-asserted-by":"publisher","first-page":"1087","DOI":"10.1007\/978-981-15-1286-5_46","article-title":"Emotion detection through eeg signals using FFT and machine learning techniques","author":"Saxena","year":"2020","journal-title":"International Conference on Innovative Computing and Communications. 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