Interpretable Machine Learning Approach Research Articles

Investigating Relationships Between Phone Use While Driving Behavior and Drivers’ Socio-demographic Characteristics: An Interpretable Machine Learning Approach

Investigating Relationships Between Phone Use While Driving Behavior and Drivers’ Socio-demographic Characteristics: An Interpretable Machine Learning Approach

Developing an explainable rockburst risk prediction method using monitored microseismicity based on interpretable machine learning approach

Developing an explainable rockburst risk prediction method using monitored microseismicity based on interpretable machine learning approach

Predicting postoperative rehemorrhage in hypertensive intracerebral hemorrhage using noncontrast CT radiomics and clinical data with an interpretable machine learning approach

In hypertensive intracerebral hemorrhage (HICH) patients, while emergency surgeries effectively reduce intracranial pressure and hematoma volume, their significant risk of causing postoperative rehemorrhage necessitates early detection and management to improve patient prognosis. This study sought to develop and validate machine learning (ML) models leveraging clinical data and noncontrast CT radiomics to pinpoint patients at risk of postoperative rehemorrhage, equipping clinicians with an early detection tool for prompt intervention. The study conducted a retrospective analysis on 609 HICH patients, dividing them into training and external verification cohorts. These patients were categorized into groups with and without postoperative rehemorrhage. Radiomics features from noncontrast CT images were extracted, standardized, and employed to create several ML models. These models underwent internal validation using both radiomics and clinical data, with the best model’s feature significance assessed via the Shapley additive explanations (SHAP) method, then externally validated. In the study of 609 patients, postoperative rehemorrhage rates were similar in the training (18.8%, 80/426) and external verification (17.5%, 32/183) cohorts. Six significant noncontrast CT radiomics features were identified, with the support vector machine (SVM) model outperforming others in both internal and external validations. SHAP analysis highlighted five critical predictors of postoperative rehemorrhage risk, encompassing three radiomics features from noncontrast CT and two clinical data indicators. This study highlights the effectiveness of an SVM model combining radiomics features from noncontrast CT and clinical parameters in predicting postoperative rehemorrhage among HICH patients. This approach enables timely and effective interventions, thereby improving patient outcomes.

An Interpretable Machine Learning Approach to Predict Survival Outcomes in Spinal and Sacropelvic Chordomas.

Retrospective, population-based cohort study. This study aimed to develop machine learning (ML) models to predict five-year and 10-year mortality in spinal and sacropelvic chordoma patients and integrate them into a web application for enhanced prognostication. Past research has uncovered factors influencing survival in spinal chordoma patients. While identifying individual predictors is important, personalized survival predictions are equally vital. Though prior efforts have resulted in nomograms aiming to serve this purpose, they cannot capture complex interactions within data and rely on statistical assumptions that may not fit real-world data. Adult spinal and sacropelvic chordoma patients were identified from the National Cancer Database. Sociodemographic, clinicopathologic, diagnostic, and treatment-related variables were utilized as predictive features. Five supervised ML algorithms (TabPFN, CatBoost, XGBoost, LightGBM, and Random Forest) were implemented to predict mortality at five and 10 years postdiagnosis. Model performance was primarily evaluated using the area under the receiver operating characteristic (AUROC). SHapley Additive exPlanations (SHAP) values and partial dependence plots provided feature importance and interpretability. The top models were integrated into a web application. From the NCDB, 1206 adult patients diagnosed with histologically confirmed spinal and sacropelvic chordomas were retrieved for the five-year mortality outcome [423 (35.1%) with five-year mortality] and 801 patients for the 10-year mortality outcome [588 (73.4%) with 10-year mortality]. Top-performing models for both of the outcomes were the models created with the CatBoost algorithm. The CatBoost model for five-year mortality predictions displayed a mean AUROC of 0.801, and the CatBoost model predicting 10-year mortality yielded a mean AUROC of 0.814. This study developed ML models that can accurately predict five-year to 10-year survival probabilities in spinal chordoma patients. Integrating these interpretable, personalized prognostic models into a web application provides quantitative survival estimates for a given patient. The local interpretability enables transparency into how predictions are influenced. Further external validation is warranted to support generalizability and clinical utility.

An Interpretable Machine Learning Approach to Predict Sensory Processing Sensitivity Trait in Nursing Students.

Sensory processing sensitivity (SPS) is a personality trait that makes certain individuals excessively sensitive to stimuli. People carrying this trait are defined as Highly Sensitive People (HSP). The SPS trait is notably prevalent among nursing students and nurse staff. Although there are HSP diagnostic tools, there is little information about early detection. Therefore, the aim of this work was to develop a prediction model to identify HSP and provide an individualized nursing assessment. A total of 672 nursing students completed all the evaluations. In addition to the HSP diagnosis, emotional intelligence, communication skills, and conflict styles were evaluated. An interpretable machine learning model was trained to predict the SPS trait. We observed a 33% prevalence of HSP, which was higher in women and people with previous health training. HSP were characterized by greater emotional repair (p = 0.033), empathy (p = 0.030), respect (p = 0.038), and global communication skills (p = 0.036). Overall, sex and emotional intelligence dimensions are important to detect this trait, although personal characteristics should be considered. The present individualized prediction model could help to predict the presence of the SPS trait in nursing students, which may be useful in conducting intervention strategies to avoid the negative consequences and reinforce the positive ones of this trait.

ENHANCING LOAN APPROVAL DECISION-MAKING: AN INTERPRETABLE MACHINE LEARNING APPROACH USING LIGHTGBM FOR DIGITAL ECONOMY DEVELOPMENT

This study aims to enhance loan approval decision-making in the digital economy using an interpretable machine learning approach. The primary research question investigates how integrating an interpretable machine learning approach can improve the accuracy and transparency of loan approval processes. We employed LightGBM, a gradient-boosting framework for loan approval classification, optimized via Random Search hyperparameter tuning and validated using 10-fold cross-validation. We incorporated the Shapley Additive exPlanations (SHAP) framework to address the challenge of interpretability in machine learning. The LightGBM model outperformed conventional algorithms (Decision Tree, Random Forest, AdaBoost, and Extra Trees) in accuracy (98.13%), precision (97.78%), recall (97.17%), and F1-score (97.48%). The study demonstrates that using an interpretable machine learning approach with LightGBM and SHAP can significantly improve the accuracy and transparency of loan approval decisions. This method offers a promising avenue for financial institutions to enhance their loan approval mechanisms, ensuring more reliable, efficient, and transparent decision-making in the digital economy. The study also underscores the importance of interpretability in deploying machine learning solutions in sectors with significant socio-economic impacts.

Integrating multi-omics data of childhood asthma using a deep association model

Childhood asthma is one of the most common respiratory diseases with rising mortality and morbidity. The multi-omics data is providing a new chance to explore collaborative biomarkers and corresponding diagnostic models of childhood asthma. To capture the nonlinear association of multi-omics data and improve interpretability of diagnostic model, we proposed a novel deep association model (DAM) and corresponding efficient analysis framework. First, the Deep Subspace Reconstruction (DSR) was used to fuse the omics data and diagnostic information, thereby correcting the distribution of the original omics data and reducing the influence of unnecessary data noises. Second, the Joint Deep Semi-Negative Matrix Factorization (JDSNMF) was applied to identify different latent sample patterns and extract biomarkers from different omics data levels. Third, our newly proposed Deep Orthogonal Canonical Correlation Analysis (DOCCA) can rank features in the collaborative module, which are able to construct the diagnostic model considering nonlinear correlation between different omics data levels. Using DAM, we deeply analyzed the transcriptome and methylation data of childhood asthma. The effectiveness of DAM is verified from the perspectives of algorithm performance and biological significance on the independent test dataset, by ablation experiment and comparison with many baseline methods from clinical and biological studies. The DAM-induced diagnostic model can achieve a prediction AUC of 0.912, which is higher than that of many other alternative methods. Meanwhile, relevant pathways and biomarkers of childhood asthma are also recognized to be collectively altered on the gene expression and methylation levels. As an interpretable machine learning approach, DAM simultaneously considers the non-linear associations among samples and those among biological features, which should help explore interpretative biomarker candidates and efficient diagnostic models from multi-omics data analysis for human complex diseases. DAM is publicly available at https://github.com/babykai12345/DAM.

Interpretable machine learning approach for exploring process-structure-property relationships in metal additive manufacturing

Process-structure-property (PSP) relationships are critical to the optimization of manufacturing processes, but establishing these relationships typically involves time- and cost- consuming experiments, especially for additive manufacturing (AM) due to the large number of process parameters involved. In this study, we develop a novel and interpretable machine learning approach for predicting, optimizing, and expanding the process window of laser powder bed fusion (LPBF) while simultaneously establishing PSP relationships, using AlSi10Mg as an example. Our iterative, error-targeted method substantially decreases the amount of experimentation required. Gaussian process regression (GPR) was employed as the predictive model, incorporating multiple input variables (e.g., process parameters, relative density, melt pool morphology, cellular structure, and grain structure), for predicting three mechanical properties (i.e., yield strength, ultimate tensile strength, and % elongation). A comparison of model predictions and experimental data outside the training scope reveals that the prediction accuracy can be improved with higher dimensional inputs and further enhanced by a multi-output model that accounts for correlations between the different mechanical properties. Additionally, the GPR kernel’s hyperparameter for each input enables feature selection and model interpretability. The proposed approach can assist with finding the most critical variables affecting mechanical performance, establishing the PSP relationships of AM fabricated alloys, and providing guidance for tailoring the final properties. The methodology presented in this study can be applied to various AM techniques and materials to broaden the process window to achieve previously unattainable mechanical properties, as well as to gain a deeper understanding of the PSP relationships.

Medication Usage Record-Based Predictive Modeling of Neurodevelopmental Abnormality in Infants under One Year: A Prospective Birth Cohort Study.

Early identification of children with neurodevelopmental abnormality is a major challenge, which is crucial for improving symptoms and preventing further decline in children with neurodevelopmental abnormality. This study focuses on developing a predictive model with maternal sociodemographic, behavioral, and medication-usage information during pregnancy to identify infants with abnormal neurodevelopment before the age of one. In addition, an interpretable machine-learning approach was utilized to assess the importance of the variables in the model. In this study, artificial neural network models were developed for the neurodevelopment of five areas of infants during the first year of life and achieved good predictive efficacy in the areas of fine motor and problem solving, with median AUC = 0.670 (IQR: 0.594, 0.764) and median AUC = 0.643 (IQR: 0.550, 0.731), respectively. The final model for neurodevelopmental abnormalities in any energy region of one-year-old children also achieved good prediction performance. The sensitivity is 0.700 (IQR: 0.597, 0.797), the AUC is 0.821 (IQR: 0.716, 0.833), the accuracy is 0.721 (IQR: 0.696, 0.739), and the specificity is 0.742 (IQR: 0.680, 0.748). In addition, interpretable machine-learning methods suggest that maternal exposure to drugs such as acetaminophen, ferrous succinate, and midazolam during pregnancy affects the development of specific areas of the offspring during the first year of life. This study established predictive models of neurodevelopmental abnormality in infants under one year and underscored the prediction value of medication exposure during pregnancy for the neurodevelopmental outcomes of the offspring.

Identifying diagnostic indicators for type 2 diabetes mellitus from physical examination using interpretable machine learning approach.

Identification of patients at risk for type 2 diabetes mellitus (T2DM) can not only prevent complications and reduce suffering but also ease the health care burden. While routine physical examination can provide useful information for diagnosis, manual exploration of routine physical examination records is not feasible due to the high prevalence of T2DM. We aim to build interpretable machine learning models for T2DM diagnosis and uncover important diagnostic indicators from physical examination, including age- and sex-related indicators. In this study, we present three weighted diversity density (WDD)-based algorithms for T2DM screening that use physical examination indicators, the algorithms are highly transparent and interpretable, two of which are missing value tolerant algorithms. Regarding the dataset, we collected 43 physical examination indicator data from 11,071 cases of T2DM patients and 126,622 healthy controls at the Affiliated Hospital of Southwest Medical University. After data processing, we used a data matrix containing 16004 EHRs and 43 clinical indicators for modelling. The indicators were ranked according to their model weights, and the top 25% of indicators were found to be directly or indirectly related to T2DM. We further investigated the clinical characteristics of different age and sex groups, and found that the algorithms can detect relevant indicators specific to these groups. The algorithms performed well in T2DM screening, with the highest area under the receiver operating characteristic curve (AUC) reaching 0.9185. This work utilized the interpretable WDD-based algorithms to construct T2DM diagnostic models based on physical examination indicators. By modeling data grouped by age and sex, we identified several predictive markers related to age and sex, uncovering characteristic differences among various groups of T2DM patients.

Air quality and urban climate improvements in the world’s most populated region during the COVID-19 pandemic

In this study, we assessed air quality (AQ) and urban climate during the mobility restrictions implemented in the Greater Tokyo Area, Japan, the world’s most populated region, in response to the COVID-19 pandemic. Observations from dense surface networks were analyzed using an interpretable machine learning approach. In parallel with a ∼50% reduction in mobility and an altered lifestyle of the population, we found limited reductions in nitrogen dioxide; decreases in fine particulate matter not entirely driven by local mobility; minor variations in ozone, with a positive (negative) tendency in areas with high (low) emissions; a decrease in air temperature consistent with mobility; and pollution levels and air temperature changes with well-defined, common spatiotemporal patterns. Specifically, cooling mainly occurred in urbanized areas with an improved AQ. Overall, although reductions in mobility were moderately effective in improving the typical indicators of urban AQ, including those known to negatively impact human health, the reductions in waste heat had a stronger impact on Tokyo’s urban heat island, suggestive of a strategy to minimize exposure to heat stress. These findings can help guide urban planning strategies and policies aimed at addressing climate change.

Risk factors affecting patients survival with colorectal cancer in Morocco: survival analysis using an interpretable machine learning approach

The aim of our study was to assess the overall survival rates for colorectal cancer at 3 years and to identify associated strong prognostic factors among patients in Morocco through an interpretable machine learning approach. This approach is based on a fully non-parametric survival random forest (RSF), incorporating variable importance and partial dependence effects. The data was povided from a retrospective study of 343 patients diagnosed and followed at Hassan II University Hospital. Covariate selection was performed using the variable importance based on permutation and partial dependence plots were displayed to explore in depth the relationship between the estimated partial effect of a given predictor and survival rates. The predictive performance was measured by two metrics, the Concordance Index (C-index) and the Brier Score (BS). Overall survival rates at 1, 2 and 3 years were, respectively, 87% (SE = 0.02; CI-95% 0.84–0.91), 77% (SE = 0.02; CI-95% 0.73–0.82) and 60% (SE = 0.03; CI-95% 0.54–0.66). In the Cox model after adjustment for all covariates, sex, tumor differentiation had no significant effect on prognosis, but rather tumor site had a significant effect. The variable importance obtained from RSF strengthens that surgery, stage, insurance, residency, and age were the most important prognostic factors. The discriminative capacity of the Cox PH and RSF was, respectively, 0.771 and 0.798 for the C-index while the accuracy of the Cox PH and RSF was, respectively, 0.257 and 0.207 for the BS. This shows that RSF had both better discriminative capacity and predictive accuracy. Our results show that patients who are older than 70, living in rural areas, without health insurance, at a distant stage and who have not had surgery constitute a subgroup of patients with poor prognosis.

Predicting Hurricane Evacuation Decisions with Interpretable Machine Learning Methods

Facing the escalating effects of climate change, it is critical to improve the prediction and understanding of the hurricane evacuation decisions made by households in order to enhance emergency management. Current studies in this area often have relied on psychology-driven linear models, which frequently exhibited limitations in practice. The present study proposed a novel interpretable machine learning approach to predict household-level evacuation decisions by leveraging easily accessible demographic and resource-related predictors, compared to existing models that mainly rely on psychological factors. An enhanced logistic regression model (that is, an interpretable machine learning approach) was developed for accurate predictions by automatically accounting for nonlinearities and interactions (that is, univariate and bivariate threshold effects). Specifically, nonlinearity and interaction detection were enabled by low-depth decision trees, which offer transparent model structure and robustness. A survey dataset collected in the aftermath of Hurricanes Katrina and Rita, two of the most intense tropical storms of the last two decades, was employed to test the new methodology. The findings show that, when predicting the households’ evacuation decisions, the enhanced logistic regression model outperformed previous linear models in terms of both model fit and predictive capability. This outcome suggests that our proposed methodology could provide a new tool and framework for emergency management authorities to improve the prediction of evacuation traffic demands in a timely and accurate manner.

Interpretable machine learning models for predicting in-hospital and 30 days adverse events in acute coronary syndrome patients in Kuwait

The relationships between acute coronary syndromes (ACS) adverse events and the associated risk factors are typically complicated and nonlinear, which poses significant challenges to clinicians' attempts at risk stratification. Here, we aim to explore the implementation of modern risk stratification tools to untangle how these complex factors shape the risk of adverse events in patients with ACS. We used an interpretable multi-algorithm machine learning (ML) approach and clinical features to fit predictive models to 1,976 patients with ACS in Kuwait. We demonstrated that random forest (RF) and extreme gradient boosting (XGB) algorithms, remarkably outperform traditional logistic regression model (AUCs = 0.84 & 0.79 for RF and XGB, respectively). Our in-hospital adverse events model identified left ventricular ejection fraction as the most important predictor with the highest interaction strength with other factors. However, using the 30-days adverse events model, we found that performing an urgent coronary artery bypass graft was the most important predictor, with creatinine levels having the strongest overall interaction with other related factors. Our ML models not only untangled the non-linear relationships that shape the clinical epidemiology of ACS adverse events but also elucidated their risk in individual patients based on their unique features.

Identifying metabolic dysfunction-associated steatotic liver disease in patients with hypertension and pre-hypertension: An interpretable machine learning approach.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is one of the most prevalent liver diseases and is associated with pre-hypertension and hypertension. Our research aims to develop interpretable machine learning (ML) models to accurately identify MASLD in hypertensive and pre-hypertensive populations. The dataset for 4722 hypertensive and pre-hypertensive patients is from subjects in the NAGALA study. Six ML models, including the decision tree, K-nearest neighbor, gradient boosting, naive Bayes, support vector machine, and random forest (RF) models, were used in this study. The optimal model was constructed according to the performances of models evaluated by K-fold cross-validation (k = 5), the area under the receiver operating characteristic curve (AUC), average precision (AP), accuracy, sensitivity, specificity, and F1. Shapley additive explanation (SHAP) values were employed for both global and local interpretation of the model results. The prevalence of MASLD in hypertensive and pre-hypertensive patients was 44.3% (362 cases) and 28.3% (1107 cases), respectively. The RF model outperformed the other five models with an AUC of 0.889, AP of 0.800, accuracy of 0.819, sensitivity of 0.816, specificity of 0.821, and F1 of 0.729. According to the SHAP analysis, the top five important features were alanine aminotransferase, body mass index, waist circumference, high-density lipoprotein cholesterol, and total cholesterol. Further analysis of the feature selection in the RF model revealed that incorporating all features leads to optimal model performance. ML algorithms, especially RF algorithm, improve the accuracy of MASLD identification, and the global and local interpretation of the RF model results enables us to intuitively understand how various features affect the chances of MASLD in patients with hypertension and pre-hypertension.

The relationship between attribute performance and customer satisfaction: An interpretable machine learning approach

Understanding the relationship between attribute performance (AP) and customer satisfaction (CS) is crucial for the hospitality industry. However, accurately modeling this relationship remains challenging. To address this issue, we propose an interpretable machine learning-based dynamic asymmetric analysis (IML-DAA) approach that leverages interpretable machine learning (IML) to improve traditional relationship analysis methods. The IML-DAA employs extreme gradient boosting (XGBoost) and SHapley Additive exPlanations (SHAP) to construct relationships and explain the significance of each attribute. Following this, an improved version of penalty-reward contrast analysis (PRCA) is used to classify attributes, whereas asymmetric impact-performance analysis (AIPA) is employed to determine the attribute improvement priority order. A total of 29,724 user ratings in New York City collected from TripAdvisor were investigated. The results suggest that IML-DAA can effectively capture non-linear relationships and that there is a dynamic asymmetric effect between AP and CS, as identified by the dynamic AIPA (DAIPA) model. This study enhances our understanding of the relationship between AP and CS and contributes to the literature on the hotel service industry.

Investigation of the open-circuit voltage of non-fullerene acceptors-based ternary organic solar cells based on interpretable machine-learning approach and chemically inspired descriptors

Ternary strategy to enhance the photovoltaic performances (e.g., open-circuit voltage (Voc), current density (Jsc), fill factor (FF), and power conversion efficiency (η)) of non-fullerene acceptors (NFAs)-based organic solar cells (OSCs) has exhibited alluring potential for next generation photovoltaic technology. However, the prediction and optimization of Voc in NFA-based ternary OSCs through expensive and time-consuming experiments or a theoretical perspective (e.g., an empirical rule for Voc prediction of the donor–acceptor binary system) is still an open challenge, mainly because of the complicated ternary photoactive layers. In this study, the prominent predictive performance (R2 > 0.7) of Voc is obtained by utilizing the technique of machine-learning combined with specific descriptors (e.g., frontier molecular orbital theory or electrophilicity index (ɷ)), for the first time forNFA-based ternary OSCs. Furthermore, the Shapley additive explanation (SHAP) model is employed to provide both global and local interpretable explanations for exploring the interpretability of the (eXtreme Gradient Boosting (XGBoost) model prediction and extracting the complex correlation between the specific descriptors and the Voc of NFA-based ternary OSCs. The proposed interpretable machine-learning strategy can contribute to the predictive modeling of highly efficient ternary OSCs based on multicomponent photoactive layers.

Unravelling CO2 Reduction Reaction Intermediates on High Entropy Alloy Catalysts: An Interpretable Machine Learning Approach to Establish Scaling Relations.

Establishment of a scaling relation among the reaction intermediates is highly important but very much challenging on complex surfaces, such as surfaces of high entropy alloys (HEAs). Herein, we designed an interpretable machine learning (ML) approach to establish a scaling relation among CO2 reduction reaction (CO2 RR) intermediates adsorbed at the same adsorption site. Local Interpretable Model-Agnostic Explanations (LIME), Accumulated Local Effects (ALE), and Permutation Feature Importance (PFI) are used for the global and local interpretation of the utilized black box models. These methods were successfully applied through an iterative way and validated on CuCoNiZnMg and CuCoNiZnSnbased HEAs data. Finally, we successfully predicted adsorption energies of *H2 CO (MAE: 0.24 eV) and *H3 CO (MAE: 0.23 eV) by using the *HCO training data. Similarly, adsorption energy of *O (MAE: 0.32 eV) is also predicted from *H training data. We believe that our proposed method can shift the paradigm of state-of-the-art ML in catalysis towards better interpretability.

Spatial distribution and source identification of potentially toxic elements in Yellow River Delta soils, China: An interpretable machine-learning approach

Identifying the driving factors and quantifying the sources of potentially toxic elements (PTEs) are essential for protecting the ecological environment of the Yellow River Delta. In this study, data from 201 surface soil samples and 16 environmental variables were collected, and the random forest (RF) and Shapley additive explanations (SHAP) methods were then combined to explore the key factors affecting soil PTEs. An innovative t-distributed random neighbor embedding-RF-SHAP model was then constructed, based on the absolute principal component score and multivariate linear regression model, to quantitatively determine PTE sources. Although average PTE concentrations did not exceed the risk control values, PTE distributions exhibited significant differences. It was found that sodium, soil organic matter, and phosphorus contents were the three most important factors affecting PTEs, and human activities and natural environmental factors both influence PTE contents by altering the soil properties. The proposed model successfully determined PTE sources in the soil, outperforming the original linear regression model with a significantly lower RMSE. Source analysis revealed that the parent material was the main contributor to soil PTEs, accounting for more than half of the total PTE content. Industrial and agricultural activities also contributed to an increase in soil PTEs, with average contributions of 19.91 % and 17.44 %, respectively. Unknown sources accounted for 10.83 % of the total PTE content. Thus, the proposed model provides innovative perspectives on source parsing. These findings provide valuable scientific insights for policymakers seeking to develop effective environmental protection measures and improve the quality of saline-alkali land in the Yellow River Delta.

Quantifying the effects of vagus nerve stimulation on gastric myoelectric activity in ferrets using an interpretable machine learning approach.

Vagus nerve stimulation (VNS) is a potential treatment option for gastrointestinal (GI) diseases. The present study aimed to understand the physiological effects of VNS on gastrointestinal (GI) function, which is crucial for developing more effective adaptive closed-loop VNS therapies for GI diseases. Electrogastrography (EGG), which measures gastric electrical activities (GEAs) as a proxy to quantify GI functions, was employed in our investigation. We introduced a recording schema that allowed us to simultaneously induce electrical VNS and record EGG. While this setup created a unique model for studying the effects of VNS on the GI function and provided an excellent testbed for designing advanced neuromodulation therapies, the resulting data was noisy, heterogeneous, and required specialized analysis tools. The current study aimed at formulating a systematic and interpretable approach to quantify the physiological effects of electrical VNS on GEAs in ferrets by using signal processing and machine learning techniques. Our analysis pipeline included pre-processing steps, feature extraction from both time and frequency domains, a voting algorithm for selecting features, and model training and validation. Our results indicated that the electrophysiological changes induced by VNS were optimally characterized by a distinct set of features for each classification scenario. Additionally, our findings demonstrated that the process of feature selection enhanced classification performance and facilitated representation learning.