# Machine Learning Techniques for Predicting the Short-Term Outcome of Resective Surgery in Lesional-Drug Resistance Epilepsy

arXiv: https://arxiv.org/abs/2302.10901 · HTML: https://mlsanity.com/paper/2302.10901/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 32 references that are in the map.
- First posted on arXiv 10 Feb 2023.

## Builds on (most cited first, 1 of 1)

- [Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review](https://mlsanity.com/paper/2002.01925/) (2020)

## Built on by (most cited first, 1 of 1)

- [PyTDC: A multimodal machine learning training, evaluation, and inference platform for biomedical foundation models](https://mlsanity.com/paper/2505.05577/) (2025)

## Abstract (arXiv)

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.