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According to the Globe Health Organization (WHO), more than 50 million people throughout the world are living with a diagnosis of epilepsy, making it perhaps of the most widely recognized neurological issue. Epileptic seizures are a leading cause of hospitalization and mortality across the globe. Accurate and prompt diagnosis is more crucial than ever given the increase in epileptic seizures all through the globe and their effect on individuals' lives. Epilepsy, cancer, diabetes, heart disease, thyroid disease, and many more are only some of the diseases for which machine learning approaches are being applied in prediction and diagnosis. Epilepsy is one ailment that may be treated early on to save a person's life. The main objective of this research is to use feature label extraction to the dataset in order to obtain the best ML models for epileptic seizures. In order to predict epilepsy, we used the techniques of logistic regression, SVM, linear SVM, KNN, and RNN in this study. The models employed in this research are accurate to varying degrees and have attributes including precision, recall, f1-score, and support. This study demonstrates that the model is able to accurately predict the occurrence of epilepsy. Our discoveries demonstrate that involving Examination highlight extraction in the dataset, the Regional Neural Network (RNN) model with 99.9998 % Training data accuracy and 97.78% Test data accuracy and 100% prediction probability of epilepsy seizure produces the best results and also the feature characteristics of RNN is better as compared to other models used in current research work.
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