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Title: :  Epilepsy Prediction Using Machine Learning
PaperId: :  25965
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 11 Issue 2 2025
DUI:    16.0415/IJARIIE-25965
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Om RautDr.Vithalrao Vikhe Patil College Of Engineering, Ahmednagar
Adesh SangleDr.Vithalrao Vikhe Patil College Of Engineering, Ahmednagar
Bhakti BarshikarDr.Vithalrao Vikhe Patil College Of Engineering, Ahmednagar
Monalika SargarDr.Vithalrao Vikhe Patil College Of Engineering, Ahmednagar
Prof. M.S.KaleDr.Vithalrao Vikhe Patil College Of Engineering, Ahmednagar

Abstract

Computer Engineering
Epilepsy prediction, feature engineering, scalp electroencephalogram (SEEG), hybrid transformer, transfer learning (TL).
Epilepsy is a neurological disorder affecting approximately 70 million people worldwide, with 85% of cases occurring in developing countries. Characterized by recurrent, unprovoked seizures, epilepsy significantly impacts a person’s quality of life and can lead to premature mortality. Electroencephalography (EEG) plays a crucial role in detecting and analyzing epileptic seizures by capturing brain activity through voltage changes. Traditional seizure detection methods are retrospective, limiting proactive response measures. This project aims to develop a machine learning-based system for real-time epilepsy prediction using EEG data, enhancing early detection and patient safety. The system preprocesses uploaded EEG data by removing null values and extracting relevant features linked to seizure activity. A Support Vector Machine (SVM) algorithm is employed to compare extracted features with trained datasets, identifying patterns indicative of epilepsy. The system architecture includes modules for data preprocessing, feature extraction, classification, and result generation. It provides real-time monitoring, seizure prediction, and alerts to patients and healthcare professionals, reducing risks and improving medical decision-making. Implemented using Python with a web-based interface powered by HTML, CSS, and SQLite, the system ensures accessibility for neurologists, healthcare providers, and researchers. Functional requirements include accurate seizure detection with at least 90% accuracy, real-time data processing, and continuous monitoring, while non-functional requirements focus on system response time, user-friendly design, and accessibility. By leveraging machine learning for epilepsy prediction, this project aims to bridge the gap between medical research and practical healthcare solutions, offering a proactive approach to managing epilepsy and enhancing patient outcomes.

Citations

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IJARIIE Om Raut, Adesh Sangle, Bhakti Barshikar, Monalika Sargar, and Prof. M.S.Kale. "Epilepsy Prediction Using Machine Learning" International Journal Of Advance Research And Innovative Ideas In Education Volume 11 Issue 2 2025 Page 517-525
MLA Om Raut, Adesh Sangle, Bhakti Barshikar, Monalika Sargar, and Prof. M.S.Kale. "Epilepsy Prediction Using Machine Learning." International Journal Of Advance Research And Innovative Ideas In Education 11.2(2025) : 517-525.
APA Om Raut, Adesh Sangle, Bhakti Barshikar, Monalika Sargar, & Prof. M.S.Kale. (2025). Epilepsy Prediction Using Machine Learning. International Journal Of Advance Research And Innovative Ideas In Education, 11(2), 517-525.
Chicago Om Raut, Adesh Sangle, Bhakti Barshikar, Monalika Sargar, and Prof. M.S.Kale. "Epilepsy Prediction Using Machine Learning." International Journal Of Advance Research And Innovative Ideas In Education 11, no. 2 (2025) : 517-525.
Oxford Om Raut, Adesh Sangle, Bhakti Barshikar, Monalika Sargar, and Prof. M.S.Kale. 'Epilepsy Prediction Using Machine Learning', International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 2, 2025, p. 517-525. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Epilepsy_Prediction_Using_Machine_Learning_ijariie25965.pdf (Accessed : ).
Harvard Om Raut, Adesh Sangle, Bhakti Barshikar, Monalika Sargar, and Prof. M.S.Kale. (2025) 'Epilepsy Prediction Using Machine Learning', International Journal Of Advance Research And Innovative Ideas In Education, 11(2), pp. 517-525IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Epilepsy_Prediction_Using_Machine_Learning_ijariie25965.pdf (Accessed : )
IEEE Om Raut, Adesh Sangle, Bhakti Barshikar, Monalika Sargar, and Prof. M.S.Kale, "Epilepsy Prediction Using Machine Learning," International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 2, pp. 517-525, Mar-App 2025. [Online]. Available: https://ijariie.com/AdminUploadPdf/Epilepsy_Prediction_Using_Machine_Learning_ijariie25965.pdf [Accessed : ].
Turabian Om Raut, Adesh Sangle, Bhakti Barshikar, Monalika Sargar, and Prof. M.S.Kale. "Epilepsy Prediction Using Machine Learning." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 11 number 2 ().
Vancouver Om Raut, Adesh Sangle, Bhakti Barshikar, Monalika Sargar, and Prof. M.S.Kale. Epilepsy Prediction Using Machine Learning. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2025 [Cited : ]; 11(2) : 517-525. Available from: https://ijariie.com/AdminUploadPdf/Epilepsy_Prediction_Using_Machine_Learning_ijariie25965.pdf
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