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

• URL: http://arxiv.org/abs/2302.10901v1
• Date: Fri, 10 Feb 2023 13:04:47 GMT
• Title: Machine Learning Techniques for Predicting the Short-Term Outcome of Resective Surgery in Lesional-Drug Resistance Epilepsy
• Authors: Zahra Jourahmad, Jafar Mehvari Habibabadi, Houshang Moein, Reza Basiratnia, Ali Rahmani Geranqayeh, Saeed Shiry Ghidary, and Seyed-Ali Sadegh-Zadeh
• Abstract summary: Seven dif-ferent categorization algorithms were used to analyze the data. The support vector machine (SVM) with the linear kernel yielded 76.1% in terms of accuracy.
• Score: 1.759008116536278
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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