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Anti-epileptic drugs are an excellent option for controlling seizure occurrence but do not work for around one-third of patients. Warning devices employing seizure prediction or forecasting algorithms could bring patients new-found comfort and quality of life. These algorithms would attempt to detect a seizure\u2019s preictal period, a transitional moment between regular brain activity and the seizure, and relay this information to the user. Over the years, many seizure prediction studies using Electroencephalogram-based methodologies have been developed, triggering an alarm when detecting the preictal period. Recent studies have suggested a shift in view from prediction to forecasting. Seizure forecasting takes a probabilistic approach to the problem in question instead of the crisp approach of seizure prediction. In this field of study, the triggered alarm to symbolize the detection of a preictal period is substituted by a constant risk assessment analysis. The present work aims to explore methodologies capable of seizure forecasting and establish a comparison with seizure prediction results. Using 40 patients from the EPILEPSIAE database, we developed several patient-specific prediction and forecasting algorithms with different classifiers (a Logistic Regression, a 15 Support Vector Machines ensemble, and a 15 Shallow Neural Networks ensemble). Results show an increase of the seizure sensitivity in forecasting relative to prediction of up to 146% and in the number of patients that displayed an improvement over chance of up to 300%. These results suggest that a seizure forecasting methodology may be more suitable for seizure warning devices than a seizure prediction one.<\/jats:p>","DOI":"10.1038\/s41598-024-56019-z","type":"journal-article","created":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T16:02:29Z","timestamp":1709827349000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Comparison between epileptic seizure prediction and forecasting based on machine learning"],"prefix":"10.1038","volume":"14","author":[{"given":"Gon\u00e7alo","family":"Costa","sequence":"first","affiliation":[]},{"given":"C\u00e9sar","family":"Teixeira","sequence":"additional","affiliation":[]},{"given":"Mauro F.","family":"Pinto","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,7]]},"reference":[{"key":"56019_CR1","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.pneurobio.2014.06.004","volume":"121","author":"P Van Mierlo","year":"2014","unstructured":"Van Mierlo, P. et al. 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