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Differentiating Mummified Thyroid Nodules From Papillary Thyroid Carcinoma: A Machine Learning Approach Using Multi-Modal Ultrasound Radiomics.

OBJECTIVES: This study aimed to establish a machine-learning model that integrates contrast-enhanced ultrasound (CEUS) radiomics, convention … mics features were extracted from the images. After feature selection, Logistic Regression (LR) and support vector machine (SVM) algorithms were used to build various models. Finally, the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the diagnostic performance and clinical utility of the models. …

Prediction and Prevalence of Depression and Anxiety in Young Adults With Specific Learning Disorders: A Machine Learning Approach in Saudi Arabia.

The current study aimed to estimate the prevalence of clinically significant depression and anxiety symptoms in young adults with DSM-5-conf … rvised ML algorithms (logistic regression, random forests, extreme gradient boosting [XGBoost], and support vector machine [SVM]) were tuned using 10-fold cross-validation within the training set and evaluated on a held-out test set (30% stratified split). These results indicate that depression and anxiety are highly prevalent among Saudi young adults with clinically co …

Machine-learning-based prediction model of type 2 diabetes using liver enzymes: a cross-sectional study.

INTRODUCTION: Although observational studies have established associations between three major liver enzymes (alanine aminotransferase (ALT) … features were combined with liver enzymes to train eight ML models, including logistic regression, support vector machine, random forest, K-nearest neighbors, classification and regression trees, gradient boosting decision tree, LightGBM, and XGBoost. Model performance was assessed using standard metrics including accuracy, precision, recall, F1-score, and area under t …

Integrative gene ontology-driven analysis of the eutopic endometrium reveals key dysregulated functionomes and pathways in endometriosis.

BACKGROUND: Endometriosis is a chronic estrogen-dependent inflammatory disorder associated with pelvic pain, infertility, and an increased r … 10 192 gene ontology (GO)-defined gene sets. Statistical analyses, exploratory factor analysis, and support vector machine (SVM)-based machine learning were used to identify dysregulated functionomes, dysfunctional pathways, and key differentially expressed genes (DEGs). RESULTS: The analysis included 196 patients with endometriosis and 246 healthy controls. Functionom …

Unveiling CMV Risk Signatures After Allogeneic Stem Cell Transplantation Through Machine Learning Approaches: A Multicenter Analysis From the GETH-TC.

Cytomegalovirus (CMV) infections remain a major complication following allogeneic hematopoietic stem cell transplantation (allo-HCT), with r … hms were evaluated using 25 repeated fivefold stratified cross-validation, including Random Forest, Support Vector Machine, Decision Trees, XGBoost, Elastic Net, and PLS-Logistic Regression. CMV DNAemia occurred in 493 recipients (53%), with 356 (72%) developing CsCMV-I and 91 (26%) progressing to CMV-R. The optimal models achieved: XGBoost for CMV DNAemia and CsCMV-I p …

Machine-learning-based determination of sex-related bladder cancer biomarkers.

Bladder cancer exhibits sex-specific behavior, occurring more frequently in males but progressing to advanced stages more commonly in female … methods: differential gene expression analysis adjusted p-value, recursive feature elimination with support vector machine, logistic regression, and an optimized random forest procedure. Gene panels were compared and aggregated across selection strategies and cross-validation folds to identify robust biomarkers for sex-specific bladder cancer development and progression …

Web-based machine learning application for ambulation prognosis in the rehabilitation phase of spinal cord injury: a retrospective multicenter cohort study.

STUDY DESIGN: A retrospective cohort study. OBJECTIVES: To develop a machine learning (ML) model to predict ambulation prognosis one year p … loped using nested 5-fold cross-validation and four algorithms: logistic regression, random forest, support vector machine, and extreme gradient boosting (XGB). Performance was assessed and compared using area under the curve (AUC), Brier score, and calibration slope, with a focus on AIS grades B and C. A web application was developed using the R package shiny and deplo …

Identification of clinical diagnostic and immune cell infiltration characteristics of acute myocardial infarction with machine learning approach.

Acute myocardial infarction (AMI) is a serious heart disease with high fatality rates. The progress of AMI involves immune cell infiltration. However, suitable clinical diagnostic biomarkers and the roles of immune cells in AMI remain unknown. Three datasets (GSE61145, GSE34198, and GSE66360) were used from Gene Expression Omnibus. Dysregulated expression of genes was screened and functionally analyzed. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify significant module genes associated with AMI. Machine learning algorithms (Support Vector Machine (SVM), Random Forest …

Development and validation of a machine learning model to predict cognitive behavioral therapy outcome in obsessive-compulsive disorder using clinical and neuroimaging data.

BACKGROUND: Cognitive behavioral therapy (CBT) is a first-line treatment for obsessive-compulsive disorder (OCD), but clinical response is d … sed functional connectivity were computed. Clinical CBT response and remission were predicted using support vector machine and random forest classifiers on clinical data only, rs-fMRI data only, and the combination of both clinical and rs-fMRI data. RESULTS: The use of only clinical data yielded an area under the ROC curve (AUC) of 0.69 for predicting remission (p = 0. …

Machine learning-based prediction of response to Ustekinumab with Crohn's disease.

BACKGROUND: A reliable approach to predict the response to Ustekinumab (UST) in patients with Crohn's disease (CD) is lacking. OBJECTIVES: This study aims to develop and validate machine learning (ML) models to predict the response to UST and further achieve personalized therapy. DESIGN: Retrospective multi-center study. METHODS: This study included 162 CD patients treated with UST between May 2022 and May 2024. Four ML algorithms (extreme gradient boosting, random forest, logistic regression, and support vector machine) were integrated to identify the optimal model, and Shapley Additive ex…

Construction and validation of machine learning models for predicting lymph node metastasis in cutaneous malignant melanoma: a large population-based study.

BACKGROUND: Lymph node status is essential for determining the prognosis of cutaneous malignant melanoma (CMM). This study aimed to develop a machine learning (ML) model for predicting lymph node metastases (LNM) in CMM. METHODS: We gathered data on 6,196 patients from the Surveillance, Epidemiology, and End Results (SEER) database, including known clinicopathologic variables, using six ML algorithms, including logistic regression (LR), support vector machine (SVM), Complement Naive Bayes (CNB), Extreme Gradient Boosting (XGBoost), RandomForest (RF), and k-nearest neighbor algorithm (kNN), to…

Applying Machine-Learning Models on Lateral Cephalogram Parameters to Classify Skeletal Classes I and III Arab Orthodontic Patients, Instead of Individualized Parameters.

AIMS: The sagittal skeletal correlation involving the maxilla and mandible is commonly assessed utilizing the A point, Nasion, B point (ANB) … c patients were analyzed in this study. Five supervised ML algorithms-linear discriminant analysis, support vector machine, K-nearest neighbor, random forest, and Classification and Regression tree-were developed and evaluated utilizing a 10-fold resampling technique. This study compared full-feature models with those using limited parameter sets, such as the Wits index …

Machine learning-based models in prediction of the radiological outcomes of vestibular schwannoma following stereotactic radiosurgery: a systematic review and meta-analysis.

BACKGROUND: Prediction of the radiological outcomes of the vestibular schwannomas (VSs) following stereotactic radiosurgery (SRS) is critica … . Most of the best performance models were ML-based (88.9 8/9). The most frequent algorithm was the support vector machine (SVM) (44.4%, 4/9). The meta-analysis revealed a pooled sensitivity rate of 86% (95%CI: 83-89%), a specificity rate of 78% (95%CI: 62-89%), and a DOR of 19.8 (95%CI: 9.12-42.9). The SROC curve exhibited an AUC of 0.845 for tumor response prediction. …

We Need to Talk About Lung Ultrasound Score: Prediction of Intensive Care Unit Admission with Machine Learning.

The admission of COVID-19 patients to the Intensive Care Unit (ICU) is largely dependent on illness severity, yet no standard criteria exist … ed in previous studies. In addition, individual LUFs were analyzed without calculating a total LUS. Support vector machine models were built, combining the available clinical data to predict ICU admissions. The application of ML models to individual LUFs outperformed standard LUS approaches reported in previous studies. Moreover, combining LU data with results from othe …

Непрозрачная онтология: нейроимAGING классификация групп диагнозов ICD-10 в UK Biobank.

Фон. Использование машинного обучения для классификации диагностических случаев по сравнению с контролем, определенным на основе диагностических онтологий, таких как десятая редакция Международной классификации болезней (ICD-10), на основе нейровизуальных признаков, сейчас является распространенной практикой в широком спектре диагностических областей. Однако сравнения, которые пересекают диагностику (transdiagnostic), таких классификаций отсутствуют. Такие трансдиагностические сравнения важны для определения специфичности классификационных моделей, установления бенчмарков и оценки ценности диа

Совпадение в аннотации оригинала
BackgroundThe use of machine learning to classify diagnostic cases versus controls defined based on diagnostic ontologies such as the Intern … from the same feature sets and against additional classifier types (k-nearest neighbors and linear support vector machine). In contrast to age classification accuracy, which was high for all feature sets, few ICD-10 diagnostic groups were classified significantly above chance (namely, demyelinating diseases based on structural neuroimaging features and depression based …

Pollution risk assessment by designing predictive binary classification models of substituted benzenes centered on data mining and machine learning techniques.

There is a growing need for industry and global regulatory agencies to develop rapid chemical safety assessment through more reliable theoretical models. Thus, quantitative structure-toxicity relationship (QSTR) models are preferred by regulators to bring chemicals to market rather than long and expensive animal testing. In this study, we evaluated four binary classification machine learning (ML) models (support vector machine, k-nearest neighbor, CART decision tree and random forest) for their ability to predict toxicity towards Tetrahymena pyriformis using 1416 benzene-derived compounds (749…

Research on odor prediction methods for coal spontaneous combustion based on E-nose technology.

This study uses an electronic nose to predict Coal Spontaneous Combustion (CSC) based on odor information. In this study, we built a Program … fferentiated among CSC stages (explaining 92.45% of the variance). The Principal Component Analysis-Support Vector Machine (PCA-SVM) model demonstrated the ability to identify characteristics of CSC odor and accurately classify stages, achieving recognition accuracy exceeding 95%. This study concludes that using E-nose technology for odor detection can be effectively us …

Construction and evaluation of machine learning-based predictive models for early-onset preeclampsia.

OBJECTIVE: To analyze the influencing factors of early-onset preeclampsia (EOPE). And to construct and validate the prediction model of EOPE using machine learning algorithm. STUDY DESIGN: Based on Python system, the data profile of 1040 pregnant women was divided into 80% training set and 20% test set. Logistic regression algorithm, XGBoost algorithm, random forest algorithm, support vector machine algorithm and artificial neural network algorithm were used to construct the EOPE prediction model, respectively, and the resulting model was validated by resampling method. Accuracy, sensitivity,…

Multi-factorial biomechanical evaluation of plate-screw fixation in femoral shaft fractures using numerical and machine learning approaches.

Plate and screw fixation is a widely used method in the surgical treatment of femoral shaft fractures; however, mechanical performance may v … ensities. Subsequently, ML algorithms including Decision Tree (DT), Multilayer Perceptron (MLP) and Support Vector Machine (SVM) were used to predict the stress and displacement values based on the numerical dataset. The finer mesh provided more accurate results. Ti-6Al-4V showed lower von Mises stress values and displacement magnitudes compared to 316 L SS. Among the M …

Stacked Deep Learning Ensemble for Multiomics Cancer Type Classification: Development and Validation Study.

BACKGROUND: Cancer is one of the leading causes of disease burden globally, and early and accurate diagnosis is crucial for effective treatm … HODS: Using a stacking ensemble learning approach, our model integrates 5 well-established methods: support vector machine, k-nearest neighbors, artificial neural network, convolutional neural network, and random forest. The methodology involves 2 main stages: data preprocessing (including normalization and feature extraction) and ensemble stacking classification. We pr …