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Altered Static and Dynamic Functional Network Connectivity and Combined Machine Learning in Stroke.

Stroke is a condition characterized by damage to the cerebral vasculature from various causes, resulting in focal or widespread brain tissue … , along with the intergroup variations in three dynamic temporal metrics, were assessed. Finally, a support vector machine (SVM) approach was employed to discriminate stroke patients from HCs using FC and FNC as classification features. Comparing the stroke group to the healthy control (HC) group, the stroke group exhibited reduced intra-network functional connectivity …

Prediction models based on machine learning algorithms for COVID-19 severity risk.

BACKGROUND: The World Health Organization has highlighted the risk of Disease X, urging pandemic preparedness. Coronavirus disease 2019 (COVID-19) could be the first Disease X; therefore, understanding the epidemiological experiences of COVID-19 is crucial while preparing for future similar diseases. METHODS: Prediction models for COVID-19 severity risk in hospitalized patients were constructed based on four machine learning algorithms, namely, logistic regression, Cox regression, support vector machine (SVM), and random forest. These models were evaluated for prediction accuracy, area under …

Дифференциация между множественным склерозом и расстройствами оптического спектра нейромиелита с многоуровневой характеристикой FMRI: анализ машинного обучения.

Традиционный статистический подход к анализу функциональных данных МРТ (rs-fMRI) в состоянии отдыха не позволяет точно различить пациентов с множественным склерозом (MS) и пациентов с расстройствами оптического спектра нейромиелита (NMOSD), что подчеркивает необходимость повышения диагностической эффективности. В этом исследовании были рассчитаны и извлечены многоуровневые функциональные метрики, включая функциональную связь в установившемся состоянии, амплитуду низких частотных колебаний (ALFF) и региональную однородность (Reho) из 116 регионов, представляющих интерес в анатомическом автомати

Совпадение в аннотации оригинала
The conventional statistical approach for analyzing resting state functional MRI (rs-fMRI) data struggles to accurately distinguish between … features to distinguish between MS and NMOSD. Compared to models based on individual MRI features, support vector machine (SVM) and logistic regression (LR) models that integrated multilevel functional features such as RSFC, ALFF, and ReHo demonstrated the highest levels of performance on the testing cohorts (SVM, AUC = 0.857; LR, AUC = 0.929). Adding structural featur …

Tongue image fusion and analysis of thermal and visible images in diabetes mellitus using machine learning techniques.

The study aimed to achieve the following objectives: (1) to perform the fusion of thermal and visible tongue images with various fusion rule … ge quality metrics are implemented to compare the classifiers' performance before and after fusion. Support vector machine outperformed the machine learning classifiers, well after fusion with an accuracy of 88.12% compared to before the fusion process (Thermal-84.37%; Visible-63.1%). VGG16 produced the classification accuracy of 94.37% after fusion and attained 90.62% …

A machine learning-based diagnosis modeling of IgG4 Hashimoto's thyroiditis.

PURPOSE: This study aims to develop a non-invasive diagnosis model using machine learning (ML) for identifying high-risk IgG4 Hashimoto's thyroiditis (HT) patients. METHODS: A retrospective cohort of 93 HT patients and a prospective cohort of 179 HT patients were collected. According to the immunohistochemical and pathological results, the patients were divided into IgG4 HT group and non-IgG4 HT group. Serum TgAb IgG4 and TPOAb IgG4 were detected by ELISAs. A logistic regression model, support vector machine (SVM) and random forest (RF) were used to establish a clinical diagnosis model for Ig…

Explainable artificial intelligence (XAI) to find optimal in-silico biomarkers for cardiac drug toxicity evaluation.

The Comprehensive In-vitro Proarrhythmia Assay (CiPA) initiative aims to refine the assessment of drug-induced torsades de pointes (TdP) ris … were computed to train various machine learning models, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forests (RF), XGBoost, K-Nearest Neighbors (KNN), and Radial Basis Function (RBF) networks. Our study's innovation is leveraging XAI, mainly through the SHAP (SHapley Additive exPlanations) method, to dissect and quantify the contribu …

Photonic platform coupled with machine learning algorithms to detect pyrolysis products of crack cocaine in saliva: A proof-of-concept animal study.

The non-invasive detection of crack/cocaine and other bioactive compounds from its pyrolysis in saliva can provide an alternative for drug a … TIR platforms were coupled to AdaBoost, Artificial Neural Networks, Naïve Bayes, Random Forest, and Support Vector Machine (SVM) algorithms tool to identify changes in the infrared salivary spectra of rats exposed to crack cocaine. The best classification of crack cocaine exposure using the salivary spectra was performed by Naïve Bayes, presenting a sensitivity of 100 % …

Altered brainstem-cortex activation and interaction in migraine patients: somatosensory evoked EEG responses with machine learning.

BACKGROUND: To gain a comprehensive understanding of the altered sensory processing in patients with migraine, in this study, we developed a … ned using weighted minimum norm estimates with spectral Granger causality analysis. This study used support vector machine algorithms to develop classification models; multimodal data (amplitude, connectivity, and scores of psychometric assessments) were applied to assess the reliability and generalisability of the identification results from the classification models. …

Convolutional Neural Networks for Recognition of Lymphoblast Cell Images.

This paper presents the recognition for WHO classification of acute lymphoblastic leukaemia (ALL) subtypes. The two ALL subtypes considered … ntify lymphocytes and ALL subtypes, and this approach is benchmarked against a dominant approach of support vector machines (SVMs) applying handcrafted feature engineering. Additionally, two traditional machine learning classifiers, multilayer perceptron (MLP), and random forest are also applied for the comparison. The experiments show that our CNN classifier delivers b …

Applications of Machine Learning Methods to Genomic Selection in Breeding Wheat for Rust Resistance.

New methods and algorithms are being developed for predicting untested phenotypes in schemes commonly used in genomic selection (GS). The prediction of disease resistance in GS has its own peculiarities: a) there is consensus about the additive nature of quantitative adult plant resistance (APR) genes, although epistasis has been found in some populations; b) rust resistance requires effective combinations of major and minor genes; and c) disease resistance is commonly measured based on ordinal scales (e.g., scales from 1-5, 1-9, etc.). Machine learning (ML) is a field of computer science that

Regional Multi-View Learning for Cardiac Motion Analysis: Application to Identification of Dilated Cardiomyopathy Patients.

OBJECTIVE: The aim of this paper is to describe an automated diagnostic pipeline that uses as input only ultrasound (US) data, but is at the … we propose two algorithms based on multi-view linear discriminant analysis and multi-view Laplacian support vector machines (MvLapSVMs). Furthermore, a novel regional multi-view approach is proposed to exploit the regional relationships between the two modalities. RESULTS: We evaluate our pipeline on the classification task of discriminating between normals and DCM pat …

Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review.

INTRODUCTION: Advanced machine learning methods might help to identify dementia risk from neuroimaging, but their accuracy to date is unclear. METHODS: We systematically reviewed the literature, 2006 to late 2016, for machine learning studies differentiating healthy aging from dementia of various types, assessing study quality, and comparing accuracy at different disease boundaries. RESULTS: Of 111 relevant studies, most assessed Alzheimer's disease versus healthy controls, using AD Neuroimaging Initiative data, support vector machines, and only T1-weighted sequences. Accuracy was highest fo…

A multiresolution clinical decision support system based on fractal model design for classification of histological brain tumours.

Tissue texture is known to exhibit a heterogeneous or non-stationary nature; therefore using a single resolution approach for optimum classi … would represent the optimal feature vector for classification. The performance was tested using the support vector machine (SVM), Bayesian and k-nearest neighbour (kNN) classifiers and a leave-one-patient-out method was employed for validation. Our method outperformed the classical energy based selection approaches, achieving for SVM, Bayesian and kNN classifiers an ove …

Personalized treatment design in the context of functional confounding.

One of the primary goals of individualized treatment rule (ITR) methodology is to identify optimal decision rules using clinical predictors. … ue of data piling, we employ the distance-weighted discrimination classifier instead of traditional support vector machines. We establish the theoretical consistency of the decision functional estimator with its risk bound. Extensive simulations and the analysis of the Alzheimer's Disease Neuroimaging Initiative dataset demonstrate the superior performance of our method …

Simulated depression risk classification from Parkinson's voice features using a self-attention-enhanced MLP architecture.

Parkinson’s disease affects both motor and non-motor functions, including vocal features that may indicate underlying mental health conditio … on risk labels. The proposed model outperforming traditional and deep learning benchmarks including Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), TabNet, CNN-LSTM, Deep Neural Network (DNN), and Explainable Boosting Machine (EBM) with an accuracy of 97%, F1-score of 98%, recall of 95%, and specificity of 100%, While EBM offers strong interpretability, the at …

Assessing the Resilience of sEMG Classifiers to Sensor Malfunction and Signal Saturation.

Surface electromyography (sEMG) is widely used for gesture recognition, yet the way classic feature-classifier pipelines fail under realisti … We exhaustively evaluated single features and all pairwise fusions with three standard classifiers (Support Vector Machine (RBF kernel), Linear Discriminant Analysis, and Random Forest) over (i) a sweep of symmetric saturation thresholds (10-6-10-1) and (ii) five single-channel dropout scenarios, reporting subject-wise dispersion rather than aggregate scores alone. This …

Real-time identification of malignant breast tissue during electrosurgical resection.

Achieving complete resection of breast cancer with clear margins remains a significant surgical challenge, particularly for infiltrating sub … or ILC spectra with true negative rates of 95,2% for NST and 85,4% for ILC. In summary, the current support vector machine (SVM) algorithm demonstrates reliable classification performance for the predominant NST subtype. However, the accuracy of the ILC classification still needs to be improved. Further refinement of the OES-based classification approach is necessary to …

Decoding Post-Stroke Cognitive Impairment After Acute Basal Ganglia Infarction: The Synergistic Role of Functional Segregation and Integration in an SVM fMRI Framework.

OBJECTIVE: To investigate whether dynamic changes in resting-state functional MRI (rs-fMRI) metrics can serve as sensitive biomarkers for di … on were analyzed using PerAF, dALFF, and dFC. Feature extraction and selection were performed using support vector machine (SVM), followed by classifier construction and evaluation. RESULTS: Patients with PSCI showed decreased PerAF in the left cerebellar Crus I (lCbeCru1) and increased dALFF in the right cerebellar Crus I and left lingual gyrus compared to non-PSCI pa …

RETRACTED: Optimizing the impact of time domain segmentation techniques on upper limb EMG decoding using multimodal features.

Neurological disorders, such as stroke, spinal cord injury, and amyotrophic lateral sclerosis, result in significant motor function impairments, affecting millions of individuals worldwide. To address the need for innovative and effective interventions, this study investigates the efficacy of electromyography (EMG) decoding in improving motor function outcomes. While existing literature has extensively explored classifier selection and feature set optimization, the choice of preprocessing technique, particularly time-domain windowing techniques, remains understudied posing a significant knowle

Kcc-ReHo and Cohe-ReHo in bipolar disorder: their associated genes and potential for diagnosis and treatment prediction.

The neural mechanisms underlying resting-state cerebral functional activity in bipolar disorder (BD) and the effects of pharmacotherapy on i … assessed using Kendall's coefficient of concordance ReHo (KCC-ReHo) and Coherence ReHo (Cohe-ReHo). Support Vector Machine (SVM) and Support Vector Regression (SVR) were employed for classification and treatment response prediction. Neuroimaging-transcriptomic analysis was conducted to explore the relationship between altered ReHo and gene expression profiles from the A …