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Real-time support vector classification and feedback of multiple emotional brain states.

An important question that confronts current research in affective neuroscience as well as in the treatment of emotional disorders is whether it is possible to determine the emotional state of a person based on the measurement of brain activity alone. Here, we first show that an online support vector machine (SVM) can be built to recognize two discrete emotional states, such as happiness and disgust from fMRI signals, in healthy individuals instructed to recall emotionally salient episodes from their lives. We report the first application of real-time head motion correction, spatial smoothing …

Neural systems predicting long-term outcome in dyslexia.

Individuals with developmental dyslexia vary in their ability to improve reading skills, but the brain basis for improvement remains largely … g gains in dyslexia. Multivariate pattern analysis (MVPA) of these two brain measures, using linear support vector machine (SVM) and cross-validation, predicted significantly above chance (72% accuracy) which particular child would or would not improve reading skills (behavioral measures were at chance). MVPA of whole-brain activation pattern during phonological process …

Biomarkers in frontotemporal lobar degeneration.

PURPOSE OF REVIEW: Recent findings assessing the utility of neuroimaging and biofluid biomarkers are reviewed that help identify patients with frontotemporal lobar degeneration (FTLD) spectrum abnormality. RECENT FINDINGS: Neuroimaging studies using T1 structural MRI and diffusion tensor imaging (DTI) distinguish between patients with FTLD and Alzheimer's disease, although the reliability of these group-level findings in individual patients has been assessed only rarely. However, innovative analyses such as support vector machine approaches are able to integrate T1 and DTI imaging results and…

Тригеминальный контакт с артерией у людей, не имеющих тригеминиальной нейронгии - исследование MR.

Основная цель этого исследования состоит в том, чтобы предложить новую нервную сеть, названную BrainNeXt, для автоматизированного обнаружения расстройств мозга с использованием изображений магнитного резонанса (MRI). Кроме того, мы намерены изучить функционирование нашей предлагаемой сети по различным видам медицинского применения. Для обеспечения эффективности классификации изображений с высоким/громким изображением мы собрали новый набор данных МРТ, принадлежащих four Классы:1Болезнь Альцгеймера,2) хроническая ишемия,3) множественный склероз и ()4) контроль. Вдохновленные ConvNeXt, мы спроек

Совпадение в аннотации оригинала
The main aim of this study is to propose a novel convolutional neural network, named BrainNeXt, for the automated brain disorders detection … in the feature selection phase. As the classifier for our patch-based FE approach, we utilized the support vector machine classifier. Our recommended BrainNeXt approach achieved an accuracy of 100% and 91.35% for training and validation. The recommended model obtained the test classification accuracy of 94.21%. To further improve the classification performance, we sugg …

Machine learning-based study on Xin-Pi simultaneous treatment formula via drug nanodelivery systems regulating macrophage polarization and TEAD2/PKM2 synergistic repair strategy in myocarditis.

BACKGROUND: Myocarditis, as an important cardiovascular disease, involves complex pathogenic mechanisms including immune dysregulation and m … ved from TCMSP, with core targets screened using machine learning algorithms such as Random Forest, Support Vector Machine, and XGBoost. A myocarditis mouse model was established, and single-cell RNA sequencing technology was utilized to analyze dynamic changes and polarization characteristics of macrophage subpopulations. Spatial transcriptomics technology was employed …

Development of a Machine Learning Algorithm-Based Predictive Model for Physical Activity Levels in Lung Cancer Survivors: A Cross-Sectional Study.

AIMS: To investigate the physical activity levels of lung cancer survivors, analyse the influencing factors, and construct a predictive mode … e logistic regression analyses. Four machine learning models-random forest, gradient boosting tree, support vector machine, and logistic regression-were developed and evaluated based on the Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC), accuracy, precision, recall, and F1 score. The best model was used to create an online computational tool usi …

Development and validation of a machine learning-based predictive model for live birth outcomes following fresh embryo transfer in patients with endometriosis.

OBJECTIVE: This study aims to develop a machine learning-based predictive model for patients with endometriosis, with the goal of precisely … sion (LR), light gradient boosting machine (LightGBM), naive Bayes model (NBM), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost), we determined the optimal hyperparameter configurations using the grid search strategy. All models were trained, and their performances were evaluated through receiver operating characteristic (ROC) cu …

Investigating Protective and Risk Factors and Predictive Insights for Aboriginal Perinatal Mental Health: Explainable Artificial Intelligence Approach.

BACKGROUND: Perinatal depression and anxiety significantly impact maternal and infant health, potentially leading to severe outcomes like pr … radient-boosting machine (LightGBM), extreme gradient boosting (XGBoost), k-nearest neighbor (KNN), support vector machine (SVM), and explainable boosting machine (EBM), were developed and compared for predictive performance. To make the black-box model interpretable, post hoc explanation techniques including Shapley additive explanations and local interpretable model-a …

Machine learning applications in forecasting patient satisfaction and clinical outcomes after carpal tunnel release: a retrospective study.

BACKGROUND: Carpal tunnel syndrome (CTS) is the most common neuropathy affecting the upper limbs. Carpal tunnel release (CTR) surgery effect … undesirable. Four machine learning algorithms—random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), and k-nearest neighbors (k-NN)—were used to predict these outcomes. The models’ performance was assessed based on accuracy, sensitivity, specificity, and area under the curve (AUC). RESULTS: The mean participant age was 50.8 years (± 7.76), w …

Data Driven Models Merging Geometric, Biomechanical, and Clinical Data to Assess the Rupture of Abdominal Aortic Aneurysms.

OBJECTIVE: Despite elective repair of a large portion of stable abdominal aortic aneurysms (AAAs), the diameter criterion cannot prevent all small AAA ruptures. Since rupture depends on many factors, this study explored whether machine learning (ML) models (logistic regression [LogR], linear and non-linear support vector machine [SVM-Lin and SVM-Nlin], and Gaussian Naïve Bayes [GNB]) might improve the diameter based risk assessment by comparing already ruptured (diameter 52.8 - 174.5 mm) with asymptomatic (diameter 40.4 - 95.5 mm) aortas. METHODS: A retrospective case-control observational st…

A machine learning-driven multitask framework for antibiotic resistance prediction in Escherichia coli, Klebsiella pneumoniae, and Enterobacter cloacae bloodstream infections among hematological malignancy patients.

BACKGROUND: Inappropriate empirical antimicrobial therapy (IEAT) significantly increases mortality in patients with resistant Gram-negative … ctive data from 1,353 HM patients with three specific Enterobacterales BSIs (2017-2023), we trained support vector machines, eXtreme Gradient Boosting, and logistic regression models through 5-fold cross-validation with hyperparameter tuning and conducting internal validation via bootstrap. Model thresholds were optimized via Pareto front analysis to minimized IEAT and …

Machine learning models for predicting metabolic syndrome to support clinical decision-making in ART-treated adults living with HIV.

BACKGROUND: Metabolic syndrome (MetS) is an emerging complication among people living with HIV (PLHIV) receiving long-term antiretroviral th … 144 weeks; 854 with complete data were included. Nine ML algorithms, including logistic regression, support vector machines, random forest, and XGBoost, were trained on 70% of the data using stratified repeated 10-fold cross-validation with hyperparameter tuning. Performance was assessed on a 30% test set using discrimination (AUC), calibration, and predictor importance …

Tumor stroma ratio-based radiomics model for predicting platinum resistance and prognosis in epithelial ovarian cancer.

BACKGROUND: The tumor-stroma ratio (TSR) has emerged as a promising prognostic biomarker in epithelial ovarian cancer (EOC); however, its pr … as performed using least absolute shrinkage and selection operator (LASSO) regression, and a linear support vector machine (SVM) classifier was constructed. The dataset was randomly divided into a training cohort (70%) and validation cohort (30%). Model performance was evaluated using five-fold cross-validation in the training cohort and tested on the independent valida …

Prediction of mortality in cancer patients with COVID-19 using machine learning methods.

This study aimed to predict mortality in cancer patients diagnosed with COVID-19 using machine learning (ML) algorithms and identify the clinical and laboratory parameters associated with mortality. Demographic, clinical, and laboratory data of cancer patients diagnosed with COVID-19 in the emergency service of Dr Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital were used. Seven ML algorithms, including decision tree, random forest, k-nearest neighbor, Naïve Bayes, eXtreme Gradient Boosting, Adaptive Boosting (AdaBoost), and support vector machines, were used to calculate t…

Research advancements in the Use of artificial intelligence for prenatal diagnosis of neural tube defects.

Artificial Intelligence is revolutionizing prenatal diagnostics by enhancing the accuracy and efficiency of procedures. This review explores AI and machine learning (ML) in the early detection, prediction, and assessment of neural tube defects (NTDs) through prenatal ultrasound imaging. Recent studies highlight the effectiveness of AI techniques, such as convolutional neural networks (CNNs) and support vector machines (SVMs), achieving detection accuracy rates of up to 95% across various datasets, including fetal ultrasound images, genetic data, and maternal health records. SVM models have dem…

Assessment of using transfer learning with different classifiers in hypodontia diagnosis.

BACKGROUND: Hypodontia is the absence of one or more teeth in the primary or permanent dentition during development, and radiographic imagin … hine learning classifiers (decision trees, discriminant analysis, logistic regression, naive Bayes, support vector machines, nearest neighbor, ensemble method, and artificial neural network). The dataset was divided into 80% for training and 20% for testing. Performance was evaluated via accuracy, recall, precision, F1-score, specificity and area under the curve (AUC) p …

Species distribution modeling to predict tsetse fly (Glossina spp.) habitat suitability in Kenya.

BACKGROUND: African animal trypanosomosis (AAT) and human African trypanosomosis (HAT) are transmitted and spread primarily by tsetse flies … tribution modeling techniques. We applied four machine learning (ML) algorithms-random forest (RF), support vector machines (SVM), maximum entropy (MaxEnt), and generalized linear models (GLM)-to predict tsetse flies habitat suitability. Additionally, we developed ensemble models that combine the predictive power of the four algorithms. Predictions were made at the genu …

Integrating MR dynamic radiomics and clinical parameters for machine learning-based prediction of short-term response to induction chemotherapy in nasopharyngeal carcinoma.

BACKGROUND AND PURPOSE: Accurate prediction of short-term response to induction chemotherapy (ICT) in nasopharyngeal carcinoma (NPC) remains … kage and selection operator (LASSO)-random forest ensemble), eight ML algorithms-XGBoost, CatBoost, support vector machine (SVM), k-nearest neighbors (KNN), and Logistic Regression,etc-were systematically evaluated. SHAP (SHapley Additive exPlanations) analysis provided model interpretability. Independent clinical predictors were identified via uni- and multivariable lo …

Early Prediction of Delirium in Postcardiac Surgery Patients: Machine Learning Model Development and External Validation.

BACKGROUND: Delirium is a frequent postoperative complication among patients who have undergone cardiac surgery and is associated with prolonged hospitalization, cognitive decline, and increased mortality. Early prediction of delirium is therefore critical for initiating timely interventions. OBJECTIVE: This study proposes the development and validation of a machine learning-based model to predict postoperative delirium in patients undergoing cardiac surgery during intensive care unit (ICU) care, facilitating the early detection of individuals at high risk of delirium and supporting clinician

Identifying predictors of student depression through validated machine learning pipelines.

BACKGROUND: Depression among student populations has become a growing public health concern, with prevalence rates ranging from 10 to 30 per … ed six baseline classification algorithms (Logistic Regression, Decision Tree, k-Nearest Neighbors, Support Vector Machine, Naive Bayes, and Random Forest) and two RUSBoost pipeline configurations on a student depression dataset comprising 27,901 records. Pipeline A employed fixed hyperparameters with constant model complexity during learning curve construction, while P …