  In this study, we developed and tested machine learning models to predict
epilepsy surgical outcome using noninvasive clinical and demographic data from
patients. Methods: Seven dif-ferent categorization algorithms were used to
analyze the data. The techniques are also evaluated using the Leave-One-Out
method. For precise evaluation of the results, the parameters accuracy,
precision, recall and, F1-score are calculated. Results: Our findings revealed
that a machine learning-based presurgical model of patients' clinical features
may accurately predict the outcome of epilepsy surgery in patients with
drug-resistant lesional epilepsy. The support vector machine (SVM) with the
linear kernel yielded 76.1% in terms of accuracy could predict results in 96.7%
of temporal lobe epilepsy (TLE) patients and 79.5% of extratemporal lobe
epilepsy (ETLE) cases using ten clinical features. Significance: To predict the
outcome of epilepsy surgery, this study recommends the use of a machine
learning strategy based on supervised classification and se-lection of feature
subsets data mining. Progress in the development of machine learning-based
prediction models offers optimism for personalised medicine access.
