High-performance prediction of epilepsy surgical outcomes based on the genetic neural networks and hybrid ieeg marker

ABSTRACT Accurately identification of the seizure onset zone (SOZ) is pivotal for successful surgery in patients with medically refractory epilepsy. The purpose of this study is to improve

the performance of model predicting the epilepsy surgery outcomes using genetic neural network (GNN) model based on a hybrid intracranial electroencephalography (iEEG) marker. We extracted

21 SOZ related markers based on iEEG data from 79 epilepsy patients. The least absolute shrinkage and selection operator (LASSO) regression was employed to integrated seven markers, selected

after testing in pairs with all 21 biomarkers and 7 machine learning models, into a hybrid marker. Based on the hybrid marker, we devised a GNN model and compared its predictive performance

for surgical outcomes with six other mainstream machine-learning models. Compared to the mainstream models, underpinning the GNN with the hybrid iEEG marker resulted in a better prediction

of surgical outcomes, showing a significant increase of the prediction accuracy from approximately 87% to 94.3% (P = 0.0412). This study suggests that the hybrid iEEG marker can improve the

performance of model predicting the epilepsy surgical outcomes, and validates the effectiveness of the GNN in characterizing and analyzing complex relationships between clinical data

variables. SIMILAR CONTENT BEING VIEWED BY OTHERS MACHINE-LEARNING FOR THE PREDICTION OF ONE-YEAR SEIZURE RECURRENCE BASED ON ROUTINE ELECTROENCEPHALOGRAPHY Article Open access 04 August

2023 MACHINE LEARNING ALGORITHM FOR PREDICTING SEIZURE CONTROL AFTER TEMPORAL LOBE RESECTION USING PERI-ICTAL ELECTROENCEPHALOGRAPHY Article Open access 18 September 2024 MACHINE LEARNING ON

INTERICTAL INTRACRANIAL EEG PREDICTS SURGICAL OUTCOME IN DRUG RESISTANT EPILEPSY Article Open access 05 March 2025 INTRODUCTION Epilepsy, a debilitating neurological disorder, affects an

estimated 60 million people worldwide and approximately 30% of those affected suffer from drug-resistant epilepsy (DRE)1. Successful surgical intervention provide better seizure control and

overall quality of life compared to medication2. However, the success rate of the epilepsy surgery was only 70% for adult3. Central to the success of surgical interventions is the accurate

identification of the seizure onset zone (SOZ). By accurately identifying the SOZ, physicians can surgically target and remove or disrupt the brain regions generating these abnormal

activities, effectively reducing the frequency and severity of seizures. Moreover, localizing the SOZ helps prevent damage to healthy brain tissue, preserving the patient’s neurological

function and cognitive abilities to the greatest extent possible. Therefore, for patients with drug-resistant epilepsy, precise localization of the SOZ is fundamental and a critical step in

surgical treatment. This process necessitates neuroimaging assessments and localization techniques to ensure optimal therapeutic outcomes and patient safety, ultimately improving the quality

of life for individuals living with epilepsy2,4. However, it is difficult to identify SOZ due to the high complexity of the brain network and the variability of epilepsy manifestations4,5.

A number of EEG markers have been employed to localize the SOZ. These markers are mainly established based on frequency band power analysis, functional connectivity analysis, and graph

theory. For example, High-frequency oscillations (HFOs), those higher than 100 Hz, have emerged as excellent surgical marker due to its contribution to the precise localization of

SOZ6,7,8,9. In addition, functional connectivity analysis has also gained popularity as a strategy for identifying the SOZ. This approach allows researchers to map out the SOZ by observing

alterations in brain activity pre-seizure and post-seizure and analyzing the functional connectivity of electrical signals across various brain nodes10. Pearson’s correlation coefficient is

often regarded as a measurement marker of intracranial functional connection11. Recently, the principle of neural fragility, which hypothesizes that epilepsy results from the disruption of

the brain’s stable connectivity, is leveraged to pinpoint the SOZ12. Moreover, graph theory is utilized to gauge alterations in brain network connectivity13,14,15. Some graph theory

constructs, including eigenvector centrality and betweenness centrality, have been used to detect the SOZ16,17. Graph theory have also been applied to high-frequency oscillation analysis,

and exhibits high performance in identifying the SOZ18. It should be noted that, however, most of the above studies only focus on a single marker and the precision of individual markers for

predicting and delineating the SOZ is often restricted, given their basis on specific assumptions and the variable recognition efficiency tied to patient-specific traits and distinct

datasets. Faced with the high complexity and variability of epilepsy manifestations, it is necessary to identify the most suitable model tailored to the characteristics of iEEG data. Hence,

the incorporation of diverse markers and development of an effective mathematical model is of paramount importance. In this study, by combining the hybrid iEEG markers, we developed a

predictive neural network model based on genetic algorithms, which is GNN19. We compared its predictive capacity for surgery outcomes with that of six other mainstream machine learning

methods, including logistic regression (LR), decision tree (DT), backpropagation neural network (BPNN), support vector machine (SVM), naive bayes model (NBM), and k-nearest neighbor

(KNN)20,21,22,23. Specifically, based on the analysis of the iEEG data from 79 epilepsy patients, we selected seven exceptional markers from 21 SOZ identification markers, including neural

fragility and 6 spectral-based features and combined them into a hybrid SOZ related marker. Leveraging the GNN model based on genetic algorithms, we formed an internal quantitative

relationship between the hybrid marker characteristics and surgical outcomes in epilepsy patients. As the result, this model predicted surgery outcomes with an accuracy of over 94%, showing

substantial benefit to clinical surgeries with ordinarily low success rates of 30–70%3. A series of comparisons revealed superior performance of the GNN over traditional machine learning

models when utilizing both hybrid and single markers, and improved performance when hybrid markers was employed various machine learning models relative to single markers. RESULTS TWENTY-ONE

SOZ MARKERS The fragility of neural nodes can be quantified using high-resolution EEG technology to identify the SOZ12. Centrality, a graph theory concept, is also used in this study. It

describes the importance of each node in the network based on the correlation of different nodes. Two different modes of centrality are used in this study: degree and eigenvector

centralities15,16, which describe the importance of nodes in various ways and help distinguish the seizure area from other areas. The centrality is calculated based on the correlation

coefficient and coherence of EEG data. The correlation coefficient is applied to time domain signals, whereas coherence measures the relevance between node signals in the frequency domain.

EEG data are transformed from the time domain to the frequency domain by Fourier transformation and then subjected to the spectrum analysis. Based on the six spectral-based features of EEG

(such as the beta band ranging from 13 to 30 Hz and high-gamma > 90 Hz), the average values are taken in a small-time window to obtain the brain energy changes. Please refer to the Method

section for the formulae used for calculating the aforementioned markers. We illustrate the gamma, neural fragility, and beta band centrality of patients 116 and 60 (Fig. 1). The heat maps

revealed that the surgical resection channel (red) for the successful patient 116 coincided with or is mostly located within the SOZ identified using the markers. Contrastingly, for patient

60 who had an unsuccessful surgery, the surgical area is not situated or is mostly not located within the SOZ identified using the markers. These findings highlight the importance of

accurately identifying the SOZ for the surgery to be successful, as visually inspecting the original EEG alone may be insufficient to make such judgments. Additionally, the visualization

through a heat map of markers, also known as EEG source imaging (ESI), assists clinicians in the selection of the surgical area24. We combine the 21 markers with seven machine-learning

models and separately trained them. To ensure optimal performance, we determine the range of the relevant hyperparameters through pretraining and set the lost weight in PyTorch to avoid

sample imbalance25. Each combination in the dataset of 79 patients is trained 10 times and 20% data is fixed a verification set. During each training, 70% of the data is allocated to the

training set and 10% is allocated to the test set. For each combination, the average accuracy rates on the training-test and verification sets are calculated (Fig. 2). According to the

results obtained, we select combinations with a top 25% accuracy on the training-test and the verification sets. This result in 9 combinations intersecting (Fig. 3A). This method ensures

that the chosen combinations have excellent performance on both sets, without exhibiting high accuracy in only one of them. Among these nine combinations, the accuracy of gamma-GNN with the

best performance is 87.85/87.95% in the training test set/verification set, while that of the ninth beta-SVM model is 80.95%/76.87%. Meanwhile, 7 markers in these 9 combinations are commonly

used markers, which include neural fragility and the averages of six frequency bands. Similarly, for each machine learning algorithm, these seven markers are also the best. Subsequently,

these seven markers are used for further constructing hybrid markers. Additionally, the GNN performs well in all combinations, dominating the top seven. The average accuracy of the GNN is

74.78%/76.58% on the training-test set/verification set, while the sub-optimal model is 75.81%/70.55% (Fig. 3B). SOZ RELATED HYBRID IEEG MARKER Directly combining the features of the seven

selected markers result in a high-dimensional feature space that is not conducive to training subsequent machine learning models. This issue is addressed using LASSO regression, which

selectively incorporates each feature into the model and reduces the dimensionality of the input features by controlling the coefficient to 0. This approach help avoid overfitting, thereby

retaining six important variables (Supplementary Fig. 2). PERFORMANCE OF GNN COMBINED WITH HYBRID IEEG MARKER We use the aforementioned hybrid SOZ markers as input for seven machine-learning

models and use the surgical outcomes of patients as the output for investigating prediction efficacy. Each combination is trained 10 times, with training-test sets and verification sets.

The average accuracy of GNN is 94.3% (Fig. 4A), which is significantly better than the decision tree of the suboptimal model by 6.59% (_P_ = 0.0412; McNemar’s test; McNemar’s test can be

used to compare the performance of two models on the same dataset)26. The maximum accuracy, upper quartile, median, lower quartile and minimum value in GNN training are 97.47%, 94.94%,

94.30%, 92.72% and 91.14%, while the highest accuracy of other models is only 93.67%. Receiver operating curves (ROC) are constructed to assess the classification ability of the models. The

greater the area under the curve (AUC), the closer the curve is to the upper left corner (0,1). The GNN has the best performance with AUC = 0.94, which is significantly higher than that of

the suboptimal model (13.25% higher; Fig. 4B; Delong’s test; maximum p-value of GNN and other models is 0.0461. The significance of the ROC curve is tested using Delong’s test27). Moreover,

considering all single SOZ markers and machine learning models previously mentioned, the GNN based on hybrid marker still exhibit superior performance, which is 7.28% higher than the

suboptimal combination (Gamma + GNN: 87.90%). The predictive ability of the GNN based on hybrid markers remains unaffected by gender and the dominant hand (Fig. 5). The probability of

predicting success represents the likelihood of a patient’s surgery is successful. A probability > 0.5 indicates that the model predicts the surgery to be successful and vice versa. The

difference in the model’s predictive ability for patients of different sexes or handedness is insignificant (Mann–Whitney U test28; gender _P_ = 0.8245 and handedness _P_ = 0.3811).

DISCUSSION In this study, we have undertaken an exhaustive comparison of common and SOZ related hybrid iEEG markers across a range of machine learning models, with a specific focus on their

effectiveness in predicting successful epilepsy surgical outcomes. Our findings reveals that the GNN, underpinned by hybrid markers, achieves an impressive accuracy of over 94%, surpassing

other models by more than 6.59% (_P_ = 0.0412). This remarkable accuracy is achieved using the GNN model paired with hybrid markers, whether in singular or combined feature. In the single

marker prediction, the spectral feature of high-gamma band has great performance, which is consistent with the current mainstream concepts such as the influence of high-frequency oscillation

on epilepsy, suggesting that high-frequency band (> 90 Hz) cannot be ignored in the study of epilepsy origin6,7,8. Epilepsy surgery is crucial for treating patients with drug-resistant

epilepsy, and improving the success rate of surgery is of utmost importance. Even a small increase in prediction accuracy would represent a significant step forward. Numerous researchers

have proposed various SOZ localization markers and epilepsy localization models to facilitate the assessment of the feasibility and efficacy of surgical plans by clinicians before surgery.

Some of them also utilize machine learning models to predict surgical outcomes, achieving accuracy rates ranging from 60 to 95%20,22,24. Our accuracy surpasses the results reported in the

majority of these research studies. Particularly noteworthy is that studies employing hybrid label-driven machine learning models consistently demonstrated strong performance on their own

datasets, affirming the viability and effectiveness of this approach23,29. Our study presents a comprehensive “combination boxing” approach with an impressive prediction accuracy of 94.30%.

With the incorporation of additional data, markers, or machine learning models, we can further enhance the precision of our predictions using this method. Machine learning is an effective

tool for recognizing data patterns with unclear mechanisms. However, training an unimproved machine learning model may yield inadequate results. Our GNN model differs from other mainstream

machine learning models due to the utilization of genetic algorithm, which trains and optimizes neural network parameters through evolutionary processes, rather than relying solely on

gradient-based optimization methods. This enables GNN model to effectively capture complex relationships within the data, enhancing its capability to handle nonlinear relationships and

high-dimensional data. The significant improvement in prediction accuracy of GNN model is a result of the synergistic interaction between its unique neural network functionality and

integrated genetic algorithm19. In this study, we compare the performance of a common neural network with that of a GNN. GNN outperformed in both single-marker and hybrid-marker scenarios.

In particular, the GNN achieve an accuracy of 75.68% for a single marker and 94.30% for hybrid markers, whereas the common neural network achieved an accuracy of only 73.18% for a single

marker and 85.70% for hybrid markers. Notably, the author of the fragility work also use deformation models of traditional random forests to predict surgical outcomes, whereas we propose a

genetic algorithm-based neural network that can expand the range of machine learning models with efficient prediction ability30. Furthermore, we discover certain counterfactual scenarios

when we use this approach to review and analyze previous surgical resection plans. In Fig. 6, the left picture indicates the patient’s original EEG during the seizure, whereas the table on

the right indicates the SOZ seizure areas identified by the seven markers. The surgical column shows the channels in the surgical area (indicated by the red font in the highlighted part of

the table). Patient pt13 exhibit successful surgical outcomes, and our GNN model with hybrid markers predict the success of the surgery. The predictions of the seven markers closely matched

the resection expectations of clinicians, indicating that the markers could simulate clinician judgment. Similarly, the surgery for patient 60 fails, and our model accurately predict

surgical failure. We observed that the predictions of the markers are strikingly incongruous with the resection area of clinicians, suggesting that our model exhibited better judgment than

the clinicians in some cases. For patient pt14, the predictions of markers matched the clinical viewpoints, and our model predicted successful surgery every time. Unfortunately, the surgery

failed, suggesting that the failure is not attributed to inaccurate positioning. This failure may have been because of other factors such as postoperative rehabilitation and the formation of

new lesions2,5. In summary, our GNN model with hybrid markers demonstrate SOZ localization abilities that are comparable to those of clinicians and provided accurate predictions of surgical

results. Currently, the inadequate availability of high-quality data and incomplete surgical information are major obstacles to accurately predicting epilepsy surgery outcomes. Our study

data are from the open data repository12,31. These samples, originating from various studies, may be subject to factors such as collection methods, selection biases, or publication biases.

While the sample size in our epilepsy SOZ recognition field may be deemed acceptable, it may still be insufficient for a large-scale model training purpose. The scarcity of data makes it

challenging to apply large-scale models and use high-dimensional inputs, which in turn complicates the validation of the reliability of each SOZ marker in other study. Insufficient data not

only relates to the current state of surgical treatment but also presents ethical considerations in medical practice. Furthermore, SOZ markers from different perspectives rely on their

assumptions and unpublished data, complicating the replication of previous studies and making it difficult to evaluate the reliability of these markers. Our manuscript delineates a study

employing an approach combining multiple markers and machine learning models, applied to iEEG data to explore the potential for localizing SOZ and predicting surgical outcomes. Our findings

stem from real clinical surgical samples across various studies, integrating 21 markers and 7 machine learning models. Considering factors such as sample representativeness, external

validity, clinical relevance, and potential limitations affecting generalizability, we believe the credibility and persuasiveness of our research outcomes. However, in no way are we

proposing that the direct clinical application of these algorithms or models. Instead, our discovery suggests that the integration of hybrid marker with the GNN model can more effectively

identify SOZ and assist in predicting surgical outcomes, underscoring the need for caution when applying singular marker and machine learning models in clinical settings. Our study has

revealed a proliferation of various SOZ identification markers, offering new possibilities for clinical translation. We have proposed an innovative approach, termed the “combination boxing”

method, which involves constructing hybrid markers and integrating refined machine learning models. This method holds promise in consolidating diverse markers to leverage their strengths,

thereby enhancing identification accuracy and predictive performance. Moreover, we plan to investigate alternative data collection channels to maximize resource utilization, thus bolstering

the credibility and impact of our study. Concurrently, we will engage in discussions regarding future research directions to further evaluate the applicability of our findings in clinical

practice. METHODS IEEG DATASETS AND DEFINITION OF SURGICAL OUTCOMES We use the datasets collected by Lohith G and Adam Li groups (namely dataset 1 and 2 in this work), which is available

from the open data website www.openneuro.org12,31. The detail information of the two datasets are summarized in Supplementary Table 1. The following data all indicate the electrodes

contained in the surgery area, which are regarded as SOZ recognized by the clinicians, and other electrodes are regarded as SOZc. The SOZ related markers are calculated based on the period

from 30 s before the seizure onset to the end for analysis. The selection of this period not only encompass the entire seizure event but also considered the iEEG information before the

seizure. The definition of surgical success is the same in both datasets. After at least 12 months, patients who had surgical outcome of Engel I or ILAE 1–2 are marked as successful outcome

and patients who had Engel II–IV or ILAE 3–6 are marked as failed outcome. DATA PROCESSING All the iEEG data are resampled to 512 Hz, notch filtered at 60 Hz with a cut-off window of 2 Hz,

and band-pass-filtered between 0.5 Hz and the Nyquist frequency with a fourth-order Butterworth filter12. All markers from 30 s before the onset of seizure to the end are calculated by

discrete time windows. And these time windows are set differently for different markers. Specifically, the time window of neutral fragility is 250 ms with 125 ms as the moving step, while

that of other markers is 2.5 s with 500 ms as the step size12,32. Furthermore, in each time window, the calculation results of other markers except the related marks of feature vector

centrality are normalized, to improve the accuracy of the machine learning models used subsequently. CALCULATION OF 21 SOZ MARKERS Inspired by different epilepsy studies, the 21 markers in

this study include the newly proposed neural fragility, frequency domain analysis in traditional research, and degree and eigenvector centralities in graph theory. NEURAL FRAGILITY Neural

fragility is a positive indicator of how easily the brain is disturbed. The more easily the brain is disturbed, the higher the fragility. Recent research has demonstrated that neural

fragility is also a marker of the SOZ. Specifically, by disturbing the stable brain network and calculating the fragility changes in each node during the seizure using iEEG data, the SOZ of

the lesion can be located12,32. The connection model of the brain network adopts the idea of using linearity to fit nonlinearity, while a channel is considered as the sum of a portion of

neurons, and its form is as follows: $$\begin{array}{c}X\left(t+1\right)=\left(A+\Delta \right)X\left(t\right),\end{array}$$ (1) where \(X\left(t\right) \left(t\in {R}^{N}\right)\)

represents the dynamic state of the brain and corresponds to the iEEG data with all channels in a time window (250 ms), matrix \(A \left(A\in {R}^{N\times N}\right)\) represents the

steady-state connection of the brain, and N is the number of iEEG channels. Seizure indicates that the brain has entered an unstable state and is disturbed, and the fragility is expressed by

the model, which can be calculated using Eq. (2): $$\begin{array}{c}\widehat{\Delta }=\left[{B}^{T}{\left(B{B}^{T}\right)}^{-1}b\right]{e}_{j}^{T},\end{array}$$ (2) where, \(B\left(\sigma

,\omega ,j\right)={\left[Im\left\{{e}_{j}{\left(A-\left(\sigma +j\omega \right)I\right)}^{-T}\right\},Re\left\{{e}_{j}{\left(A-\left(\sigma +j\omega

\right)I\right)}^{-T}\right\}\right]}^{T}\), and normalization by \(\Delta =\left({\text{max}}\widehat{\Delta }-\widehat{\Delta }\right)/{\text{max}}\widehat{\Delta }\)12,32. Normalization

is to better measure the differences in fragility of different parts of the brain at the same time. 6 SPECTRAL-BASED FEATURES The frequency bands for brain waves are strict, and different

frequency bands correspond to different functional states of the brain. We divided the frequency bands into the following categorizations: Delta frequency band: 0.5–4 Hz; Theta frequency

band: 4–8 Hz; Alpha frequency band: 8–13 Hz; Beta frequency band: 13–30 Hz; Gamma frequency band: 30–90 Hz; High-gamma frequency band: > 90 Hz. Notably, HFOs are included in the

high-gamma frequency band. We apply a one-dimensional Fourier transform to convert the original EEG data into frequency signals. The Fourier transform is a linear integral transform, which

employs a sine wave to split the signal (Eq. 3): $$\begin{array}{c}F\left(u\right)={\int }_{-\infty }^{\infty }f\left(x\right){e}^{-2\pi uxi}dx.\end{array}$$ (3) Under the frequency signal,

we consider the average value of each electrode as the spectral-based features in different time windows and then normalize them (Eq. 4). This normalization method is employed to prepare an

electrode with a larger value that becomes a part of SOZ and an isotropic agent with the significance of other markers.

$$\begin{array}{c}F\left(u\right)=\frac{F\left(u\right)-{\text{min}}F\left(u\right)}{{\text{max}}F\left(u\right)-{\text{min}}F\left(u\right)}.\end{array}$$ (4) CORRELATION COEFFICIENTS AND

COHERENCE The correlation coefficients and coherence are calculated as the Eq. (5), respectively. And they are calculated separately within each frequency band. In the Eq. (5), (i, j) is the

channel number, \(Cov\) means calculating covariance, G means calculating cross spectral density16,33.

$$\begin{array}{l}\left\{\begin{array}{l}{Corr}_{ij}=\frac{Cov\left({x}_{i},{x}_{j}\right)}{{\sigma }_{{x}_{i}}{\sigma }_{{x}_{j}}}\\

{Coh}_{ij}\left(f\right)=\frac{{\left|{G}_{ij}\left(f\right)\right|}^{2}}{{G}_{ii}\left(f\right){G}_{jj}\left(f\right)}\end{array}\right..\end{array}$$ (5) DEGREE CENTRALITY AND EIGENVECTOR

CENTRALITY Centrality measures the importance of a node in a complex graph network. Both of degree centrality and eigenvector centrality have demonstrated good performance in epilepsy

localization, and they are calculated as the Eq. (6), respectively15,17. $$\begin{array}{l}\left\{\begin{array}{l}{DC}_{i}=\frac{\sum_{j=1}^{{N}_{i}}{x}_{ij}}{{N}_{i}-1}\\

{EVC}_{k}\left(i\right)=\frac{1}{\lambda \left(k\right)}\sum_{j}{\overline{Coh} }_{ij}\left(k\right)\cdot {EVC}_{k}\left(j\right)\end{array}.\right.\end{array}$$ (6) FEATURE EXTRACTION Each

marker first needs to undergo feature extraction to use it as an input for the model and is represented as a two-dimensional data matrix consisting of time and electrode channels. To

facilitate feeding the features into machine learning models, we perform dimension reduction by the following two steps. First, to extract 20 quantiles, we regard the surgical resection area

marked in the dataset as SOZ and the remaining as SOZc. We accumulate the marker values of the SOZ and SOZc channels in each time dimension and then extract 10 quantiles of each channel in

the time sequence respectively to obtain 20 values (the first 10 are SOZ and the last 10 are SOZc)12. Secondly, for the principal component analysis. We obtain the principal components from

20 values using 85% as the threshold of cumulative contribution rate. In this way, we have achieved dimensionality reduction for each marker. To construct hybrid SOZ markers, we combine the

seven selected outstanding markers as the input of machine learning models. If these markers are directly merged, it will generate too many features, so we perform Lasso regression to select

the important variables whose coefficients are non-zero as the model inputs. Lasso regression introduces an L1 regularization penalty term into the loss function as follows,

$$\begin{array}{c}Loss\left(\omega \right)=\sum_{i=1}^{N}{\left({y}_{i}-{\omega }^{T}{x}_{i}\right)}^{2}+\lambda {\Vert \omega \Vert }_{1} ,\end{array}$$ (7) where \(\omega\) represents the

weight coefficients of each feature. Under this framework, it updates the weight coefficients of some features to zero in the linear regression process, thereby achieving the goal of feature

selection. GENETIC NEURAL NETWORK In this study, a genetic algorithm is applied to optimize the weight of the neural network instead of the back propagation (BP) algorithm of the

traditional network. In the traditional BP algorithm training, the algorithm gradually adjusts weights along the direction of local improvement, resulting in the problem of local minima.

This issue is avoided using a genetic algorithm based on biological evolution to initialize the neural network with several groups of different weights and to jump out of the local minima

with a certain probability. This genetic algorithm is more likely to approach the global optimal solution19. Specifically, we apply this algorithm to train the weights between the layers of

the single hidden layer neural network. We set the individuals of the population in the genetic algorithm as the weights of the neural network and defined the fitness function as

\(fitness={\text{max}}\left\{{MSE}_{train},{MSE}_{test}\right\}\), where \({MSE}_{train}\) and \({MSE}_{test}\) denote the mean square error on the training and test sets, respectively. Its

main application process is explained as follows and the training process is depicted as a flowchart in Supplementary Fig. 1. MAINSTREAM MACHINE-LEARNING MODELS The other six

machine-learning models include logistic classification, decision tree, neural network, support vector machine, naive bayes model, and neighborhood algorithm. These six algorithms are all

implemented by Python’s SK-learn34. The training of each model is divided into the training set (70%), the test set (10%) and the verification set (20%, it can also be called lock-box set).

In the training set, 20% cross-validation and weight adjustment of the loss function are employed to prevent over-fitting. After training, the accuracy of the model is calculated in the

training-test and verification sets. All 147 combined models are trained 10 times, and the training-test sets are re-divided for each training session and the verification set is fixed.

Finally, the average accuracy of the model in the training-test set and the average accuracy in the verification set are calculated. The training mode of the hybrid feature model is the same

as above. STATISTICS ANALYSIS McNemar’s test is employed to compare the surgical prediction accuracy among these models. This test is particularly suitable for comparing the performance of

models using the same dataset26. Delong’s test is used to assess the significance of the ROC curve. This test helps determine whether there are significant differences in the performance of

the models based on the ROC curve analysis27. Furthermore, the Mann–Whitney U test investigates whether there is any influence of gender and dominant hand on the prediction ability of the

model. This test allows us to determine if there are any statistically significant differences in the model’s performance based on these factors28. A significance level of P < 0.05 is

considered to indicate statistical significance. All the statistical analyses are conducted using Python version 3.9. DATA AVAILABILITY We obtained data from publicly available databases

openneuro.org. The datasets are provided Lohith G and Adam Li groups respectively (ds004100 (https://openneuro.org/datasets/ds004100) and ds003029 (https://openneuro.org/datasets/ds003029)).

The use of the database adhered to the terms of service and ethical guidelines set forth by the database providers. The data were anonymized and handled in accordance with privacy

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Foundation of China (No. 82272112), Zhejiang Province general undergraduate universities “14th Five-Year” teaching reform project (No. jg20220300) and general project of teaching reform of

Zhejiang Chinese Medicine University (No. YB22020). FUNDING This work was supported by the STI2030-Major Projects (2022ZD0204600), the National Natural Science Foundation of China

(82272112), Zhejiang Province general undergraduate universities “14th Five-Year” teaching reform project (jg20220300) and general project of teaching reform of Zhejiang Chinese Medicine

University (YB22020). AUTHOR INFORMATION Author notes * These authors contributed equally: Lipeng Sun, Chen Feng and En Zhang. AUTHORS AND AFFILIATIONS * Second Clinical Medical School,

Zhejiang Chinese Medical University, Hangzhou, China Lipeng Sun * Department of Neurosurgery, School of Medicine, Second Affiliated Hospital, Zhejiang University, Hangzhou, China Chen Feng 

& Junming Zhu * School of Medicine, Epilepsy Center, Second Affiliated Hospital, Zhejiang University, Hangzhou, China Chen Feng & Junming Zhu * IDG/McGovern Institute for Brain

Research, Beijing Normal University, Beijing, China En Zhang * State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, Beijing, China

En Zhang * Department of Physical and Environmental Sciences, University of Toronto, Toronto, Canada Huan Chen * School of Pharmacy, Zhejiang Chinese Medical University, Hangzhou, China

Weifeng Jin * School of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China Li Yu * Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China Chen

Feng * Key Laboratory of Drug Safety Evaluation and Research of Zhejiang Province, Hangzhou Medical College, Hangzhou, China Li Yu Authors * Lipeng Sun View author publications You can also

search for this author inPubMed Google Scholar * Chen Feng View author publications You can also search for this author inPubMed Google Scholar * En Zhang View author publications You can

also search for this author inPubMed Google Scholar * Huan Chen View author publications You can also search for this author inPubMed Google Scholar * Weifeng Jin View author publications

You can also search for this author inPubMed Google Scholar * Junming Zhu View author publications You can also search for this author inPubMed Google Scholar * Li Yu View author

publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS Lipeng Sun and Chen Feng performed model training, data curation, and comparative study. En Zhang

participated in the interpretation and preprocessing of clinical data and the analysis of model results. Huan Chen and Weifeng Jin discussed the results concerning the clinical context.

Junming Zhu and Li Yu performed the model building, program writing and article editing. All authors reviewed the manuscript. CORRESPONDING AUTHORS Correspondence to Junming Zhu or Li Yu.

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visit http://creativecommons.org/licenses/by/4.0/. Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Sun, L., Feng, C., Zhang, E. _et al._ High-performance prediction of epilepsy

surgical outcomes based on the genetic neural networks and hybrid iEEG marker. _Sci Rep_ 14, 6198 (2024). https://doi.org/10.1038/s41598-024-56827-3 Download citation * Received: 05

February 2024 * Accepted: 12 March 2024 * Published: 14 March 2024 * DOI: https://doi.org/10.1038/s41598-024-56827-3 SHARE THIS ARTICLE Anyone you share the following link with will be able

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initiative KEYWORDS * Genetic neural network * Surgical outcome prediction * Seizure onset zone * Hybrid marker * Model comparison