Seamless Vital Signs-Based Continuous Authentication Using Machine Learning

Intraoperative hypotension (IOH) is a critical complication during surgical procedures that can lead to severe adverse outcomes including myocardial injury, acute kidney injury, and increased mortality. Early prediction of hypotensive events remains a significant challenge in perioperative medicine. This study leverages the Medical Informatics Operating Room Vitals and Events Repository (MOVER) dataset, a comprehensive collection of intraoperative physiological signals and clinical events, to develop and evaluate machine learning models for predicting hypotensive events 5, 10, and 15 minutes before onset.The MOVER dataset contains high-frequency vital sign measurements including heart rate, blood pressure, oxygen saturation, and respiratory metrics from over 5,000 surgical procedures. Extensive preprocessing and feature engineering were performed to extract statistical, temporal, and interaction features across multiple time windows. Multiple machine learning algorithms were implemented and compared including XGBoost, Random Forest, Histogram-based Gradient Boosting (HGB), Support Vector Machines (SVM) with RBF kernel, Long Short-Term Memory (LSTM) networks, Multilayer Perceptron (MLP), and K-Nearest Neighbors (KNN).Experimental results demonstrate that XGBoost achieves the highest predictive performance with an accuracy of 94.2%, precision of 93.8%, recall of 94.5%, and AUC-ROC of 0.973 for 5-minute prediction windows. Performance remained strong for 10-minute (AUC-ROC = 0.942) and 15-minute (AUC-ROC = 0.908) predictions. Feature importance analysis revealed that mean arterial pressure (MAP) trends, heart rate variability, shock index, and time since last vasopressor administration were the most significant predictors. Error analysis identified borderline MAP values and rapid hemodynamic changes as primary sources of misclassification.The proposed models demonstrate strong potential for real-time clinical decision support systems to alert anesthesiologists of impending hypotensive events, enabling proactive interventions and improved patient outcomes. This research represents the first comprehensive comparison of multiple machine learning algorithms on the MOVER dataset for hypotension prediction, providing a foundation for future clinical implementation and prospective validation studies.

Diabetes is a non-communicable disease affecting people of all ages worldwide, therefore, early detection using machine learning techniques is crucial. This study aims to predict diabetes using multiple machine learning algorithms, performance metrics, and holdout validation on an Egyptian dataset. The dataset was divided into four age groups, including paediatric, early adulthood, middle age, and geriatric. Ten algorithms were applied and validated using 80:20, 70:30, and 60:40 split ratios with accuracy, precision, and recall as evaluation metrics. Results showed that Random Forest, Extra Trees, and Support Vector Machine performed best in the paediatric group, while Gradient Boosting, Random Forest, and Support Vector Machine achieved superior performance in early adulthood, middle age, and geriatric groups. In contrast, Decision Tree, K-Nearest Neighbors, and AdaBoost consistently demonstrated lower performance. Further analysis reveals that classification performance varies significantly across age groups, with the middle age and geriatric groups achieving the highest accuracy above 0.99, followed by the paediatric group 0.98–0.99, while early adulthood exhibits comparatively lower performance due to increased class overlap. Confusion matrix results indicate strong diagonal dominance in higher-performing groups, reflecting better class separability, whereas performance heatmaps confirm that top models maintain a balanced trade-off between accuracy, precision, and recall with minimal variation across different data splits. Feature importance analysis shows that higher performing models rely on a small number of dominant predictors, particularly in the middle age and geriatric groups, while more distributed feature contributions in early adulthood reduce predictive effectiveness. Therefore, the findings demonstrate that ensemble methods provide robust and consistent performance, and that age-based dataset segmentation enhances classification accuracy and model stability.

Lung cancer is one of the three deadliest diseases in the world and has rapidly developed. Based on this, researchers conducted research to predict the factors that influence lung cancer. One method to identify this is using data mining methods and classification techniques. Researchers used several popular algorithms in classification to make comparisons of the most accurate algorithms for lung cancer classification. The algorithms used include K-Nearest Neighbor, Random Forest Classifier, Logistic Regression, Linear SVM, Naïve Bayes, Decision Tree, Random Forest, Gradient Boosting, Kernel SVM, and MLPClassifier. The researcher used this algorithm because, in the research that the researcher found on the Kaggle platform, he examined the comparison of the algorithm using the breast cancer dataset. In previous studies, their researchers used SVM, which obtained an accuracy of 96.47%, Neural Networks of 97.06%, and Naïve Bayes with an accuracy of 91.18% to study breast cancer. The difference from previous research is that this study uses several existing algorithms in Machine Learning such as K-Nearest Neighbor, Random Forest Classifier, Logistic Regression, Linear SVM, Naïve Bayes, Decision Tree, Random Forest, Gradient Boosting, Kernel SVM, and MLPClassifier. In addition, this research was conducted to see whether the results of the accuracy of the algorithm that the researchers carried out using the lung cancer dataset had different results. The results of this study found that the more accurate algorithms were Random Forest and Gradient Boosting with an accuracy value of 100%, whereas in previous studies, it was the same. Still, Gradient Boosting had a higher accuracy value than Random Forest. Then, based on the data used in this study, the most influencing factors in predicting a diagnosis of lung cancer are obesity and coughing up blood. The results of this study found that the more accurate algorithms were Random Forest and Gradient Boosting with an accuracy value of 100%, whereas in previous studies, it was the same. Still, Gradient Boosting had a higher accuracy value than Random Forest. Then, based on the data used in this study, the most influencing factors in predicting a diagnosis of lung cancer are obesity and coughing up blood. The results of this study found that the more accurate algorithms were Random Forest and Gradient Boosting with an accuracy value of 100%, whereas in previous studies, it was the same. Still, Gradient Boosting had a higher accuracy value than Random Forest. Then, based on the data used in this study, the most influencing factors in predicting a diagnosis of lung cancer are obesity and coughing up blood.

Detection of congestive heart failure from short-term heart rate variability segments using hybrid feature selection approach

ObjectiveTo evaluate the diagnostic and prognostic predictive value of the pan-immune-inflammation value (PIV) and triglyceride-glucose (TyG) index in premature coronary artery disease (PCAD).MethodsThis study analyzed data from 26,883 patients admitted with chest pain at Liuzhou People's Hospital (January 2014 to December 2020), with 5,653 patients included after screening. Multiple machine learning algorithms, including Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Lasso regression, Random Forest (RF), and logistic regression, were applied to identify PCAD-related variables, which were integrated into a decision tree model. Propensity score matching (PSM) ensured cohort comparability. The Mime1 package facilitated ensemble feature selection and visualization, while optimal PIV and TyG cutoff values were determined via Receiver Operating Characteristic (ROC) analysis for 36-month survival subgroup analysis.ResultsLogistic regression identified PIV [odds ratio [OR] 2.651, 95% CI [to be specified], P < 0.001] and TyG [OR 1.003, 95% CI (to be specified), P < 0.001] as PCAD risk factors. The decision tree model, incorporating PIV, TyG, and white blood cell count (WBC), achieved an accuracy of 0.88 and an area under the ROC curve (AUC) of 0.86 for PCAD diagnosis. Survival analysis over 36 months revealed that low PIV and TyG levels were associated with reduced all-cause mortality, whereas elevated levels correlated with poorer prognosis (P < 0.001), with TyG showing a pronounced effect.ConclusionThe combined evaluation of PIV, TyG, and WBC offers robust diagnostic and prognostic value for PCAD, with elevated PIV and TyG levels indicating a poor prognosis, underscoring their potential as clinical biomarkers.

Type 2 diabetes mellitus has been associated with elevated high-sensitivity C-reactive protein (hsCRP), but the possible implication of ambulatory heart rate (HR) variability in enhancing chronic subclinical inflammation biomarkers remains to be elucidated. We aimed to evaluate the correlations between HR variability and coefficient of variance assessed during 24-hour ambulatory blood pressure monitoring (ABPM) and hsCRP in type 2 diabetes and control subjects. The observational study included type 2 diabetes (n=75) and control (n=11) subjects. HR variability was calculated as standard deviation of mean HR during daytime, nighttime and 24-hour periods. The coefficient of variation was calculated using standard deviation and mean HR. Nighttime HR dipping was calculated using daytime and nighttime HR variability. We found higher hsCRP in type 2 diabetes compared to controls. Also, we found that daytime, nighttime and 24-hour HR variability and coefficient of variance were lower in the type 2 diabetes group compared with the control group, while type 2 diabetes subjects receiving β-blockers had even lower ambulatory HR variability and coefficient of variance. Subjects with diabetic neuropathy, retinopathy and atherosclerotic cardiovascular disease had lower HR variability and coefficient of variance compared to their peers without disease. We observed that daytime and 24-hour HR variability inversely correlated with hsCRP, while all HR variability parameters inversely correlated with hypertension duration in the study population. Nighttime HR dipping inversely correlated with duration of type 2 diabetes and hypertension. Our results suggest that 24-hour ambulatory HR variability and coefficient of variance are significantly correlated with chronic inflammation evaluated using hsCRP in type 2 diabetes and control subjects, and these findings deserve further investigations.

To construct machine learning prediction model for sepsis-associated encephalopathy (SAE), and analyze the application value of the model on early identification of SAE risk in elderly septic patients. Patients aged over 60 years with a primary diagnosis of sepsis admitted to intensive care unit (ICU) from 2008 to 2023 were selected from Medical Information Mart for Intensive Care-IV 2.2 (MIMIC-IV 2.2). Demographic variables, disease severity scores, comorbidities, interventions, laboratory indicators, and hospitalization details were collected. Key factors associated with SAE were identified using univariate Logistic regression analysis. The data were randomly divided into training and validation sets in a 7 : 3 ratio. Multivariable Logistic regression analysis was conducted in the training set and visualized using a nomogram model for prediction of SAE. The discrimination of the model was evaluated in the validation set using the receiver operator characteristic curve (ROC curve), and its calibration was assessed using calibration curve. Furthermore, multiple machine learning algorithms, including multi-layer perceptron (MLP), support vector machine (SVM), naive bayes (NB), gradient boosting machine (GBM), random forest (RF), and extreme gradient boosting (XGB), were constructed in the training set. Their predictive performance was subsequently evaluated on the validation set. Taking the XGB model as an example, the interpretability of the model through the SHapley Additive exPlanations (SHAP) algorithm was enhanced to identify the key predictive factors and their contributions. A total of 2 204 septic patients were finally enrolled, of whom 840 developed SAE (38.1%). A total of 21 variables associated with SAE were screened through univariate Logistic regression analysis. Multivariable Logistic regression analysis showed that endotracheal intubation [odds ratio (OR) = 0.40, 95% confidence interval (95%CI) was 0.19-0.88, P < 0.001], oxygen therapy (OR = 0.76, 95%CI was 0.53-0.95, P = 0.023), tracheotomy (OR = 0.20, 95%CI was 0.07-0.53, P < 0.001), continuous renal replacement therapy (CRRT; OR = 0.32, 95%CI was 0.15-0.70, P < 0.001), cerebrovascular disease (OR = 0.31, 95%CI was 0.16-0.60, P < 0.001), rheumatic disease (OR = 0.44, 95%CI was 0.19-0.99, P < 0.001), male (OR = 0.68, 95%CI was 0.54-0.86, P = 0.001), and maximum anion gap (AG; OR = 0.95, 95%CI was 0.93-0.97, P < 0.001) were associated with an decreased probability of SAE, and age (OR = 1.05, 95%CI was 1.03-1.06, P < 0.001), acute physiology score III (APSIII; OR = 1.02, 95%CI was 1.01-1.02, P < 0.001), Oxford acute severity of illness score (OASIS; OR = 1.04, 95%CI was 1.03-1.06, P < 0.001), and length of hospital stay (OR = 1.01, 95%CI was 1.01-1.02, P < 0.001) were associated with an increased probability of SAE. A nomogram model was constructed based on these variables. In the validation set, ROC curve analysis showed that the model achieved an area under the ROC curve (AUC) of 0.723, and the calibration curve showed good consistency between the predicted probability of the model and the observed probability. Among the machine learning algorithms, including MLP, SVM, NB, GBM, RF, and XGB, the SVM model and RF model demonstrated relatively good predictive performance, with AUC of 0.748 and 0.739, respectively, and the sensitivity was both exceeding 85%. The predictive performance of the XGB model was explained through SHAP analysis, and the results indicated that APSIII score (SHAP value was 0.871), age (SHAP value was 0.521), and OASIS score (SHAP value was 0.443) were important factors affecting the predictive performance of the model. The machine learning-based SAE prediction model exhibits good predictive capability and holds significant application value for the early identification of SAE risk in elderly septic patients.

Consumer wearables for mental state assessment face scepticism regarding data quality. This study evaluated whether a consumer-grade EEG/PPG wearable headband (Neurostellar’s wearable device) can reliably capture mental states in real-world settings. We compared the wearable device's EEG (AF7, AF8) and PPG-derived heart rate variability (HRV) signals with a laboratory-standard EEG/ECG device across 31 resting sessions (15 adults), assessing univariate correlations across 18 EEG, 12 HRV features. Participants then completed four cognitive and affective tasks while using the wearable device, providing subjective feedback. Novel multimodal features, combining short-window individual metrics with long-window complexity from multi-feature cluster sequences, were utilised in machine learning (Random Forest, Gradient Boosting) to classify tasks and predict changes in subjective relaxation and focus. Neurostellar’s wearable device showed moderate-to-strong correlations with lab measurements for numerous EEG (eyes-closed: 12; eyes-open: 14) and HRV (eyes-closed: 9; eyes-open: 6) features in &gt;50% of recordings. The advanced multimodal features significantly classified all tasks (Random Forest: z=13.08-13.11; Gradient Boosting: z=13.00-13.16, all p&lt;0.001) and predicted combined relaxation/focus ratings (Random Forest: z=2.37; Gradient Boosting: z=1.79, all p&lt;0.05) versus chance. These findings suggest that consumer wearables, employing robust multimodal feature engineering, can effectively capture psychophysiological changes and track mental states, such as focus and relaxation, in naturalistic environments, offering potential for real-world neuroscience applications. Limitations include a modest sample size and restricted demographic diversity, which warrant further research.

In recent years, cardiovascular disease (CVD) has emerged as a leading cause of mortality worldwide, highlighting the urgent need for reliable predictive models to support early diagnosis and preventive care. This study conducts a comparative analysis of various machine learning models to assess CVD risk, focusing on their ability to accurately identify individuals at high risk based on clinical and demographic data. We evaluate multiple supervised learning algorithms, including logistic regression, random forests, support vector classifiers, K-nearest neighbors, gradient boosting, and AdaBoost, comparing their predictive performance in terms of accuracy, precision, recall, and F1-score. Additionally, we propose a novel hybrid model that combines Random Forest (RF) for feature selection and Deep Neural Networks (DNN) for classification, aiming to leverage the strengths of both approaches for more accurate CVD prediction. Our findings demonstrate that ensemble models such as random forests and gradient boosting achieve superior performance, with high accuracy (0.99) and balanced precision and recall values, outperforming simpler models like logistic regression and support vector classifiers. The hybrid model further enhances prediction accuracy, achieving 92.4% accuracy, 91.7% precision, 93.0% recall, and an AUC-ROC score of 96.0%. The analysis also underscores the importance of data preprocessing techniques, including normalization and handling of missing values, in optimizing model accuracy and stability. Notably, K-nearest neighbors also performed exceptionally well with a high F1-score across classes, highlighting its robustness for this task. This study provides a detailed examination of each model's strengths and limitations, including the proposed hybrid model, offering valuable insights for healthcare practitioners and data scientists in selecting effective machine learning models for CVD risk prediction. By integrating these models into healthcare systems, real-time risk prediction can be enhanced, ultimately supporting clinical decision-making and advancing personalized care in cardiovascular health. Keywords— cardiovascular disease, machine learning, risk assessment, predictive models, supervised learning, healthcare

Accurately predicting concrete compressive strength is fundamental for optimizing mix designs, ensuring structural integrity, and advancing sustainable construction practices. Increased demands for safer, more durable infrastructure necessitate effective predictive concrete compressive strength models. This research aims to compare the effectiveness of six machine learning models such as Linear Regression, Random Forest, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Gradient Boosting, and XGBoost to predict concrete compressive strength. Used a dataset of 1030 instances with varying mixture compositions, conducted extensive exploratory data analysis, and applied feature engineering and data scaling to enhance model performance. Assessments were performed with a 5-fold cross-validation approach with the R-squared (R²) metric. In addition, the SHAP value is used to understand the influence of each feature on the compressive strength results. The results revealed that XGBoost significantly outperformed other models, achieving an average R² value of 0.9178 with a standard deviation of 0.0296. Notably, Random Forest and Gradient Boosting also demonstrated robust capabilities. Based on our experiment, these models effectively predicted compressive strengths close to actual measured values, confirming their practical applicability in civil engineering. SHAP values provided insights into the significant impact of age and cement quantity on model outputs. These results highlight the advanced ensemble methods' effectiveness in concrete compressive strength prediction and underscore the importance of feature engineering in enhancing model accuracy.

Background/Objectives: Smartphone addiction (SA) and gaming addiction (GA) have become risk factors for individuals of all ages in recent years. Especially during adolescence, it has become very difficult for parents to control this situation. Physical activity and the effective use of free time are the most important factors in eliminating such addictions. This study aimed to test a new machine learning method by combining routine regression analysis with the gradient-boosting machine (GBM) and random forest (RF) methods to analyze the relationship between SA and GA with leisure time management (LTM) and the enjoyment of physical activity (EPA) among adolescents. Methods: This study presents the results obtained using our developed GBM + RF hybrid model, which incorporates LTM and EPA scores as inputs for predicting SA and GA, following the preprocessing of data collected from 1107 high school students aged 15-19 years. The results were compared with those obtained using routine regression results and the lasso, ElasticNet, RF, GBM, AdaBoost, bagging, support vector regression (SVR), K-nearest neighbors (KNN), multi-layer perceptron (MLP), and light gradient-boosting machine (LightGBM) models. In the GBM + RF model, probability scores obtained from GBM were used as input to RF to produce final predictions. The performance of the models was evaluated using the R2, mean absolute error (MAE), and mean squared error (MSE) metrics. Results: Classical regression analyses revealed a significant negative relationship between SA scores and both LTM and EPA scores. Specifically, as LTM and EPA scores increased, SA scores decreased significantly. In contrast, GA scores showed a significant negative relationship only with LTM scores, whereas EPA was not a significant determinant of GA. In contrast to the relatively low explanatory power of classical regression models, ML algorithms have demonstrated significantly higher prediction accuracy. The best performance for SA prediction was achieved using the Hybrid GBM + RF model (MAE = 0.095, MSE = 0.010, R2 = 0.9299), whereas the SVR model showed the weakest performance (MAE = 0.310, MSE = 0.096, R2 = 0.8615). Similarly, the Hybrid GBM + RF model also showed the highest performance for GA prediction (MAE = 0.090, MSE = 0.014, R2 = 0.9699). Conclusions: These findings demonstrate that classical regression analyses have limited explanatory power in capturing complex relationships between variables, whereas ML algorithms, particularly our GBM + RF hybrid model, offer more robust and accurate modeling capabilities for multifactorial cognitive and performance-related predictions.

Spinal cord injury (SCI) is a prevalent and serious complication among patients with spinal tuberculosis (STB) that can lead to motor and sensory impairment and potentially paraplegia. This research aims to identify factors associated with SCI in STB patients and to develop a clinically significant predictive model. Clinical data from STB patients at a single hospital were collected and divided into training and validation sets. Univariate analysis was employed to screen clinical indicators in the training set. Multiple machine learning (ML) algorithms were utilized to establish predictive models. Model performance was evaluated and compared using receiver operating characteristic (ROC) curves, area under the curve (AUC), calibration curve analysis, decision curve analysis (DCA), and precision-recall (PR) curves. The optimal model was determined, and a prospective cohort from two other hospitals served as a testing set to assess its accuracy. Model interpretation and variable importance ranking were conducted using the DALEX R package. The model was deployed on the web by using the Shiny app. Ten clinical characteristics were utilized for the model. The random forest (RF) model emerged as the optimal choice based on the AUC, PRs, calibration curve analysis, and DCA, achieving a test set AUC of 0.816. Additionally, MONO was identified as the primary predictor of SCI in STB patients through variable importance ranking. The RF predictive model provides an efficient and swift approach for predicting SCI in STB patients.

Many patients with severe obstructive sleep apnea (OSA) do not complain of excessive daytime sleepiness (EDS), possibly due to increased sympathetic nervous activity (SNA) and accompanying heightened alertness. We hypothesized that in patients with OSA, those without subjective EDS (Epworth Sleepiness Scale, ESS score < 11) would have higher very low frequency (VLF) heart rate variability (HRV) during sleep, reflecting greater sympathetic heart rate modulation than patients with an ESS score ≥ 11. Patients with severe OSA (AHI ≥ 30: 26 with and 65 without heart failure) were divided into those with and without EDS. Heart rate (HR) signals were acquired in stage 2 sleep during periods of recurrent apneas and hypopneas and submitted to coarse graining spectral analysis, which extracts harmonic, neurally mediated contributions to HRV from total spectral power. Because the apnea-hyperpnea cycle entrains muscle SNA at VLF (0 to 0.04 Hz), VLF power was our principal between-group comparison. Subjects without EDS had higher harmonic VLF power (944 ± 839 vs 447 ± 461 msec(2), p = 0.003) than those with EDS, irrespective of the presence or absence of heart failure (1218 ± 944 vs 426 ± 299 msec(2), p = 0.043, and 1029 ± 873 vs 503 ± 533 msec(2), p = 0.003, respectively). ESS scores correlated inversely with VLF power in all (r = -0.294, p = 0.005) and in heart failure subjects (r = -0.468, p = 0.016). Patients with severe OSA but without EDS have higher VLF-HRV than those with EDS. This finding suggests that patients with severe OSA but without EDS have greater sympathetic modulation of HRV than those with EDS that may reflect elevated adrenergically mediated alertness. Taranto Montemurro L; Floras JS; Picton P; Kasai T; Alshaer H; Gabriel JM; Bradley TD. Relationship of heart rate variability to sleepiness in patients with obstructive sleep apnea with and without heart failure.

Fatigue is important in aviation because the risks associated with fatigue pose a threat to many people. The purpose of this study is to investigate the possibility of using a wearable ECG device to detect mental fatigue in pilots. 22 healthy individuals were recruited from Beihang University and each subject wore a Polar H10 heart rate band with a Polar IGNITE sports watch to record heart rate variability (HRV) data. To stimulate the fatigue state, each subject performed a simulated flight experiment of 1h and the number of blinks was recorded by the face recognition method as a label for fatigue. HRV data and blink frequency were collected every 5 minutes throughout the experiment. After feature selection, four HRV indicators SDNN, LF_HF, LF and VLF were extracted for subsequent analysis. Five machine learning algorithms: support vector machine (SVM), Random Forest, AdaBoost, K-nearest neighbor (KNN), Naïve Bayes (NB) were used to build a classifier for automatic detection of pilot fatigue. Naïve Bayes (NB) had the best performance and had an average CV F1 score of 0.9065 on the training set and 0.9250 on the test set.

The prediction of wind energy generation is important to enhance the performance and dependability of renewable energy systems due to the rising demand for wind-generated electricity and advancements in wind energy technology competitiveness. This study leverages advanced machine learning (ML) and some other statistical and deep learning based time series forecasting models to enhance the accuracy of wind energy predictions. This comprehensive analysis includes nine ML models—Linear Regression, Random Forests (RF), Gradient Boosting Machines (GBM), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), AdaBoost, XGBoost, Support Vector Regression (SVR), and Neural Networks—as well as Four time-series forecasting models—ARIMA, Temporal Convolutional Networks (TCNs), Long Short-Term Memory (LSTM) networks and GRU. Each ML model underwent rigorous cross-validation to ensure optimal performance. The assessment criteria utilized here comprised the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the R² Score. It was found that among the nine ML models, Random Forests, GBM and KNN consistently provided superior accuracy and robustness, making them the top choices for wind energy prediction whereas the performance of linear regression, SVM and SVR were very poor for the considered dataset. From the experiment, Random Forest, GBM, and KNN showed the best performance with low MSE values of 0.77, 1.95, and 1.51 respectively, while other models had MSEs above 7.5, with AdaBoost reaching 30. Their RMSEs (0.88, 1.40, 1.23) and MAEs (0.093, 0.73, 0.10) also indicate strong predictive accuracy compared to the rest.In this paper, time series forecasting, TCNs, LSTM and GRU networks showed strong capabilities in capturing temporal dependencies and trends within the wind energy data. Visualization techniques were employed to compare model performances comprehensively, providing clear insights into their predictive power. Therefore, this present study offers a robust framework for researchers and practitioners aiming to leverage machine learning and time series forecasting in the realm of renewable energy prediction.