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Brain Microstructural Changes in Patients with Amnestic mild Cognitive Impairment : Detected by Neurite Orientation Dispersion and Density Imaging (NODDI) Combined with Machine Learning.

PURPOSE: This study investigated brain microstructural changes in patients with amnestic mild cognitive impairment (aMCI) by retrospectively … ning algorithms such as K‑nearest neighbor (KNN), logistic regression (LR), random forest (RF), and support vector machine (SVM) were used to evaluate the diagnostic efficacy of NODDI parameters in predicting aMCI. The differences in the NODDI parameter values between the aMCI and HC groups were analyzed using the independent sample t‑test, False discovery rate (FDR) co …

[Feasibility of ultrasound radiomics-based models for classification of hepatic echinococcosis].

OBJECTIVE: To investigate the feasibility of establishment of ultrasound radiomics-based models for classification of hepatic echinococcosis … re created based on two classifiers, including kernel logistic regression (KLR) and medium Gaussian support vector machine (MGSVM). The receiver operating characteristic (ROC) curves were plotted, and the sensitivity, specificity and areas under the curves (AUC) of the created machine learning models for classification of hepatic echinococcosis were calculated. RESULTS …

Development and Validation of a Magnetic Resonance Imaging-Based Machine Learning Model for TMJ Pathologies.

The purpose of this study was to propose a machine learning model and assess its ability to classify TMJ pathologies on magnetic resonance ( … ic regression (LR), random forest (RF), decision tree (DT), k-nearest neighbors (KNN), XGBoost, and support vector machine (SVM) were used for model building which could predict the TMJ pathologies. The performance of models was evaluated by sensitivity, specificity, and ROC curve. KNN and RF classifiers were found to be the most optimal machine learning model for the p …

Assessing the risk of dengue severity using demographic information and laboratory test results with machine learning.

BACKGROUND: Dengue virus causes a wide spectrum of disease, which ranges from subclinical disease to severe dengue shock syndrome. However, estimating the risk of severe outcomes using clinical presentation or laboratory test results for rapid patient triage remains a challenge. Here, we aimed to develop prognostic models for severe dengue using machine learning, according to demographic information and clinical laboratory data of patients with dengue. METHODOLOGY/PRINCIPAL FINDINGS: Out of 1,581 patients in the National Cheng Kung University Hospital with suspected dengue infections and subj

Above-ground biomass estimation in tropical dry deciduous forests using multi-sensor data from ALOS-2 and sentinel-2 with advanced machine learning approaches.

Accurate estimation of above-ground biomass (AGB) is essential for sustainable forest resource management and carbon stock assessment. This study focuses on the Pembi Forest, Telangana, India, where synthetic aperture radar (SAR) data from ALOS-2 PALSAR-2 L-band and multispectral data from Sentinel-2 were integrated with biophysical information to develop AGB prediction models. Predictor variables included SAR backscatter and polarization-derived features, Sentinel-2 spectral indices and band combinations, principal components, leaf area index (LAI), and global canopy height. Light Gradient Bo

Uncovering Placental Endocrine Dysregulation in Gestational Diabetes Mellitus Using Bioinformatics and Machine Learning.

BACKGROUND: Gestational diabetes mellitus (GDM) is a common metabolic disorder, posing serious health risks to both mother and fetus. This study aims to explore the placental endocrine mechanisms involved in GDM. METHODS: The training dataset GSE203346 and endocrine-related gene sets from the Molecular Signatures Database were used to identify endocrine-related differentially expressed genes (ER-DEGs). Support vector machine recursive feature elimination (SVM-RFE) and Boruta algorithms were employed to identify hub genes. Diagnostic performance was analyzed using the receiver operating charac

An object-based custom ensemble framework for automated water body extraction using Sentinel-2 imagery.

Accurate surface water monitoring across India's diverse landscapes is often hindered by the limitations of pixel-based classification and static indexing. This study introduces an object-based framework integrating DBSCAN clustering with supervised learning classification for automated water extraction using Sentinel-2 imagery. To ensure regional generalizability, the model was trained on a multi-site dataset spanning 20 distinct geographical locations across India. Out of 5 machine learning (ML)models, XGBoost outperformed other ML architectures, including Random Forest and state vector mach

Predicting sperm retrieval success in salvage testicular sperm extraction: a machine learning perspective.

This multicenter retrospective observational study aimed to identify predictive factors for successful secondary testicular sperm extraction (TESE) using advanced machine learning (ML) models. Data from 503 infertile men who underwent secondary TESE between 2021 and 2024 after a previous unsuccessful attempt were analyzed. Preoperative characteristics and laboratory findings were assessed using ten ML algorithms, including Xtreme Gradient Boosting, Random Forest, Gradient Boosting, Decision Tree, AdaBoost, Logistic Regression, Multi-Layer Perceptron, Support Vector Machine, K-Nearest Neighbors…

An explainable machine learning model for predicting osteoporotic fragility fractures: a retrospective study in South China.

BACKGROUND: Osteoporotic fragility fractures often lead to hospitalization and impose a significant economic burden. To investigate the clin … o filter these features. Then we evaluated six machine learning algorithms (XGBoost, Random Forest, Support Vector Machine, CatBoost, LightGBM, and Logistic Regression) to construct the best predictive model. In order to explain the model, SHAP (SHapley Additive exPlanations) was used to assess the importance of each attribute. RESULTS: LightGBM achieved the highest tr …

Leveraging AI for facioscapulohumeral muscular dystrophy prediction and omics biomarker identification.

Facioscapulohumeral muscular dystrophy (FSHD) is an autosomal dominant muscle disorder characterized by a complex genetic etiology, variable … and miRNA expression profiles. Key predictive features were identified using Random Forest and the Support Vector Machine-Recursive Feature Elimination (SVM-RFE) methods. Performance evaluations demonstrated the robustness of the ML classifiers, with logistic regression consistently achieving the robust predictive accuracy in distinguishing FSHD from healthy conditions …

Assessing the Clinical and Functional Status of COPD Patients Using Speech Analysis During and After Exacerbation.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) affects breathing, speech production, and coughing. We evaluated a machine learning analysis of speech for classifying the disease severity of COPD. METHODS: In this single centre study, non-consecutive COPD patients were prospectively recruited for comparing their speech characteristics during and after an acute COPD exacerbation. We extracted a set of spectral, prosodic, and temporal variability features, which were used as input to a support vector machine (SVM). Our baseline for predicting patient state was an SVM model using self-r…

Machine Learning for Multi-Omics Characterization of Blood Cancers: A Systematic Review.

Artificial Intelligence and machine learning are increasingly used to interrogate complex biological data. This systematic review evaluates … multiple myeloma (18). Other hematological diseases were less frequently studied. Methods included Support Vector Machines, Random Forests, and deep learning (28, 25, and 24 studies). Multi-omics integration was reported in 23 studies. External validation occurred in 31 studies, and explainability in 19. The median diagnostic area under the curve was 0.87 (interquartil …

USP21 is involved in the development of chronic hepatitis B by modulating the immune microenvironment.

Hepatitis B virus (HBV) infection is a global public health challenge that alters the immune microenvironment of the liver and drives diseas … e learning algorithms, random forest, least absolute shrinkage and selection operator, and selected support vector machine recursive feature elimination, we identified the PCD signature genes associated with CHB from the candidate genes. We further validated that ubiquitin-specific peptidase 21 could differentiate CHB patients with different natural courses by receiver …

Quantitative ultrasound classification of healthy and chemically degraded ex-vivo cartilage.

In this study, we explore the potential of ten quantitative (radiofrequency-based) ultrasound parameters to assess the progressive loss of c … ance in distinguishing healthy cartilage from trypsin-treated samples, with an accuracy of 60%. The support vector machine demonstrated excellent accuracy (96%) in differentiating healthy cartilage from collagenase-degraded samples. Histological and mechanical analyses further confirmed these findings, with collagenase having a more pronounced impact on both mechanical …

Development, evaluation and comparison of machine learning algorithms for predicting in-hospital patient charges for congestive heart failure exacerbations, chronic obstructive pulmonary disease exacerbations and diabetic ketoacidosis.

BACKGROUND: Hospitalizations for exacerbations of congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD) and diabetic ketoacidosis (DKA) are costly in the United States. The purpose of this study was to predict in-hospital charges for each condition using machine learning (ML) models. RESULTS: We conducted a retrospective cohort study on national discharge records of hospitalized adult patients from January 1st, 2016, to December 31st, 2019. We constructed six ML models (linear regression, ridge regression, support vector machine, random forest, gradient boosting and ext…

Distinct plasma phosphorylated-tau proteins profiling for the differential diagnosis of mild cognitive impairment and Alzheimer's disease by plasmonic asymmetric nanobridge-based biosensor.

The differential diagnosis between mild cognitive impairment (MCI) and Alzheimer's disease (AD) has been highly demanded for its effectivene … ith a limit of detection in the attomolar regime for quantitative analysis of p-tau. By introducing support vector machine (SVM)-based machine learning algorithm, the improved diagnostic system was achieved for prediction of healthy controls, MCI, and AD groups with an accuracy of 94.47 % by detecting various p-tau species levels in human plasma. Thus, our proposed PAN- …

Machine Learning for Enhanced COPD Diagnosis: A Comparative Analysis of Classification Algorithms.

Background: In the United States, chronic obstructive pulmonary disease (COPD) is a significant cause of mortality. As far as we know, it is … ne learning (ML) classifiers, such as Logistic Regression (LR), Gradient Boosting Classifier (GBC), Support Vector Machine (SVM), Gaussian Naïve Bayes (GNB), Random Forest Classifier (RFC), K-Nearest Neighbors Classifier (KNC), Decision Tree (DT), and Artificial Neural Network (ANN). These models were applied to a dataset comprising 1603 patients after being referred fo …

Development and validation of radiomics models for the prediction of diagnosis of classic trigeminal neuralgia.

The study aims to develop a magnetic resonance imaging (MRI)-based radiomics model for the diagnosis of classic trigeminal neuralgia (cTN). … radiomics features. Machine learning methods were established, including LASSO logistic regression, support vector machine (SVM), and Adaboost methods, evaluating each model's diagnostic abilities using 10-fold cross-validation. All the models showed excellent diagnostic ability in predicting trigeminal neuralgia. A prospective study was conducted, 20 cTN patients and 2 …

A comparison of machine learning methods to classify radioactive elements using prompt-gamma-ray neutron activation data.

The detection of illicit radiological materials is critical to establishing a robust second line of defence in nuclear security. Neutron-cap … e tree-based algorithms (Decision Trees, Random Forest and AdaBoost) have consistently outperformed Support Vector Machine and K-Nearest Neighbours. Based on the results presented, AdaBoost is the preferred classifier to analyse data containing PGAA spectral information due to the high recall and minimal false negatives reported in the minority class.

Усиленная диагностика заболевания множественным склерозом на основе подхода, основанного на ансамблях

Множественный склероз (MS) — болезнь, наносящая удар по центральной нервной системе. Согласно самым последним статистическим данным MS Atlas 2.8 million люди во всем мире диагностировали MS. Недавно в рамках исследований стали изучаться методы машинного обучения для прогнозирования НР с использованием различных данных. Целью настоящего документа является разработка комплексного подхода к диагностике MS с использованием профилей выражения генов при одновременном решении проблемы классового дисбаланса, связанной с данными. Предлагается иерархический комплексный подход с использованием методов го

Совпадение в аннотации оригинала
Multiple Sclerosis (MS) is a disease attacking the central nervous system. According to MS Atlas’s most recent statistics, there are more th … ed. This approach adopts a heterogeneous voting approach using two base learners, random forest and support vector machine. Experiments show that our approach outperforms state-of-the-art methods, with the highest recorded accuracy being 92.81% and 93.5% with BoostFS and DEGs for feature selection, respectively. Conclusively, the proposed approach is able to efficiently …