Levetiracetam Monotherapy in Treatment of Generalized Epilepsies Based on Machine Learning Models - Khudhur, Ala Saeed; Khudhur, Srwa Saeed (Iraqi Journal of Science, 2026)
Epilepsy is recurrent unprovoked seizures with incidents of about 50 new cases per year per 100,000 people. Generalized seizures indicate abnormal electrical discharges starting synchronously from both sides of the cerebral hemispheres and spreading to the other neuronal brain networks. This study included an analysis of 200 diagnosed generalized epilepsies patients aged from 14 to 50 years old. The male-to-female ratio was 2:3, in which 40% were males and 60% were females . From Erbil Hospital, doctors collected the data during 2018-2024 manually from the outpatient clinic. The study aimed to test Levetiracetam (LEV) drug on 200 patients to predict its effectiveness in the treatment of generalized epilepsies and determine the side effects of LEV by using machine learning models. The results showed that LEV could become the first-line monotherapy and be effective for the treatment of generalized epilepsies in patients aged 14-50 years regardless of sex. It has fewer side effects than the old-generation antiepileptics. Accordingly, machine learning models were applied like support vector machine (SVM), which could achieve good results in terms of accuracy and F1_score of 0.935 and 0.903 for patients who suffer from side-effects of Levetiracetam tablet.