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For accurate detection of epileptic seizures, numerous scientific techniques have been used with EEG data, and most of these techniques have produced promising results. For EEG signal classification with a high classification accuracy rate, the present research proposes an enhanced machine learning-based epileptic seizure detection model. The present research provides a hybrid Improved Adaptive Neuro-Fuzzy Inference System (IANFIS)-Light Gradient Boosting Machine (LightGBM) technique for automatically detecting and diagnosing epilepsy from EEG data. The experimental findings were supported by EEG records made available by the German University of Bonn and scalp EEG data acquired at Children\u2019s Hospital Boston. The suggested IANFIS-LightGBM, according to the results, offers the most significant classification accuracy ratings in both situations.<\/jats:p>","DOI":"10.3233\/jifs-233430","type":"journal-article","created":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T12:34:47Z","timestamp":1705062887000},"page":"2463-2482","source":"Crossref","is-referenced-by-count":2,"title":["Automatic detection of epileptic seizure using machine learning-based IANFIS-LightGBM system"],"prefix":"10.1177","volume":"46","author":[{"given":"D.","family":"Saranya","sequence":"first","affiliation":[{"name":"Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India"}]},{"given":"A.","family":"Bharathi","sequence":"additional","affiliation":[{"name":"Bannari Amman Institute of Technology, Alathukombai, Sathyamangalam, Tamil Nadu, 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