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Ferroptosis-related hub genes and immune cell dynamics as diagnostic biomarkers in age-related macular degeneration.

BACKGROUND: Age-related Macular Degeneration (AMD) is widely acknowledged as a principal cause of vision loss in the elderly. Currently, the … d as the primary focus of the study. Subsequent screening of these 19 genes using LASSO regression, Support Vector Machine (SVM), and Random Forest algorithms identified four hub genes: FADS1, TFAP2A, AKR1C3, and TTPA. Consequently, we utilized cigarette smoke extract (CSE) to either stimulate retinal pigment epithelial (RPE) cells in vitro or administer it via intravit …

Predicting survival outcomes in advanced pancreatic cancer using machine learning methods.

The prognosis for pancreatic cancer (PC) is poor, with a 5-year survival rate of approximately 10%. Methods such as machine learning (ML) ca … tient received had the highest F-score in predicting that patient's survival. Among ML methods, the support vector machine (SVM) kernel method had the highest accuracy rate (87%) in predicting whether patients were deceased. When the feature selection methods were combined with the SVM kernel ML method, patients' survival statuses could be predicted with an accuracy rat …

Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.

Ulcerative colitis (UC) is a chronic inflammatory bowel disease with an idiopathic origin, characterized by persistent mucosal inflammation. … ing least absolute shrinkage and selection operator (LASSO) Cox regression, random forest (RF), and support vector machine (SVM). Receiver operating characteristic (ROC) analysis was utilized to evaluate the diagnostic accuracy of each gene. Subsequently, Single sample GSEA (ssGSEA) was executed to explore the relationships within immune cell infiltration, UC subtypes, …

Ultrasound-based radiomics and clinical factors-based nomogram for early intracranial hypertension detection in patients with decompressive craniotomy.

OBJECTIVE: This study aims to develop and validate a nomogram that combines traditional ultrasound radiomics features with clinical paramete … ature selection methods were applied to construct predictive models using logistic regression (LR), support vector machine (SVM), random forest (RF), and K-Nearest Neighbors (KNN). Clinical-ultrasound variables were incorporated into the model through univariate and multivariate logistic regression. A combined nomogram was developed by integrating radiomics features wit …

Radiomics in Pituitary Adenomas: A Systematic Review of Clinical Applications and Predictive Models.

Background: Radiomics offers quantitative, high-dimensional data from conventional imaging and holds promise for improving diagnosis and tre … extraction tool (20 studies, 41%). Machine learning was employed in 43 studies (88%), predominantly support vector machines (16 studies, 33%), random forests (9 studies, 18%), and logistic regression (9 studies, 18%). Deep learning methods were applied in 17 studies (35%). Regarding diagnostic performance, 22 studies (45%) reported an (AUC) ≥0.85 in test datasets. Exter …

Analysis and validation of novel biomarkers related to palmitoylation in adenomyosis.

BACKGROUND: Adenomyosis, a common gynecological disorder in women of reproductive age, is characterized by endometrial invasion into the myo … ression network analysis (WGCNA), Least Absolute Shrinkage (LASSO), random forest (RF) methods, and Support Vector Machine-recursive feature elimination (SVM-RFE)-we identified three overlapping diagnostic genes through comprehensive analysis. Meanwhile, the diagnostic value of each biomarker was assessed using the receiver operating characteristic curve analysis in the …

Construction and validation of a machine learning-based risk prediction model for invasive mechanical ventilation in AECOPD patients complicated with respiratory failure.

OBJECTIVE: This study aimed to create and validate a machine learning (ML) model to predict the likelihood of invasive mechanical ventilation (IMV) in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) complicated by respiratory failure. METHODS: Data from patients diagnosed with AECOPD and respiratory failure were retrospectively extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV). A total of 551 cases were split 7:3 into a training set (385 cases) for model construction and an internal validation set (166 cases). The IMV served as the

Predictive models and determinants of mortality among T2DM patients in a tertiary hospital in Ghana, how do machine learning techniques perform?

BACKGROUND: The increasing prevalence of type 2 diabetes mellitus (T2DM) in lower and middle - income countries call for preventive public h … d build predictive models respectively. The performance of machine learning (ML) techniques such as support vector machine (SVM), decision tree, k nearest neighbor (kNN), eXtreme Gradient Boosting (XGBoost) and logistic regression were evaluated using the best-fitting predictive model for T2DM mortality. RESULTS: Of the 328 participants, 183 (55.79%) were female, and t …

Evaluation of factors predicting transition from prediabetes to diabetes among patients residing in underserved communities in the United States - A machine learning approach.

INTRODUCTION: Over one-third of the population in the United States (US) has prediabetes. Unfortunately, underserved population in the United States face a higher burden of prediabetes compared to urban areas, increasing the risk of stroke and heart disease. There is a gap in the literature in understanding early predictors of diabetes among patients with prediabetes living in underserved communities in the United States. Hence, this study's objective is to identify factors influencing the transition from prediabetes to diabetes in rural or underserved communities using a machine learning appr

Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.

INTRODUCTION: Sepsis, a critical medical condition resulting from an irregular immune response to infection, leads to life-threatening organ … arning algorithms: random forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machine-Recursive Feature Elimination (SVW-RFE). The utilization of the receiver operating characteristic curve in conjunction with the nomogram model served to authenticate the discriminatory strength and efficacy of the key genes. CIBERSORT was utilized t …

Computational Analyses Identified Three Diagnostic Biomarkers Associated With Programmed Cell Death for Lung Adenocarcinoma.

Background: The high morbidity and mortality of lung adenocarcinoma (LUAD) are partly caused by a lack of sensitive and reliable molecular m … e identified 160 module genes and 5934 DEGs. Then, eight hub genes were selected applying LASSO and support vector machine-recursive feature elimination (SVM-RFE) analyses. Further, FGR, TLR4, and NLRC4, which showed an area under the ROC curve (AUC) > 0.7, were determined as the diagnostic biomarkers for LUAD. Interestingly, they were all low expressed in LUAD samples. …

Graph Neural Network Learning on the Pediatric Structural Connectome.

PURPOSE: Sex classification is a major benchmark of previous work in learning on the structural connectome, a naturally occurring brain grap … eural network (multi-layer perceptron), and two standard machine learning models (random forest and support vector machine) were trained. Architecture exploration experiments were conducted to evaluate the impact of network depth, pooling techniques, and skip connections on the ability of GNN models to capture connectomic patterns. Models were assessed across a range of …

Real-time highway crash prediction based on SHAP-RFECV and electronic toll collection data: a new feature selection strategy.

Real-time crash prediction has emerged as a critical area of research in traffic safety, aiming to improve safety performance through proact … chmark models, i.e., logistic regression, long short-term memory, convolutional neural network, and support vector machine, were developed to further validate the effectiveness of the feature selection method. The results demonstrate that the proposed SHAP-RFECV algorithm outperformed traditional feature selection methods such as RFECV, least absolute shrinkage and sele …

Diagnosis of Schizophrenia and Its Subtypes Using MRI and Machine Learning.

PURPOSE: The neurobiological heterogeneity present in schizophrenia remains poorly understood. This likely contributes to the limited succes … accuracy) in estimating the patient's label for a new dataset acquired domestically. Using a linear support vector machine (SVM) on 62 features obtained from MRMR, patients with schizophrenic subtypes were classified with an accuracy of 64%. The highest Spearman correlation coefficient between the neuroimaging features and behavioral assessments was observed between the …

Prediction of Postoperative Venous Thromboembolism in Patients With Traumatic Brain Injury: Model Development and Validation Study.

BACKGROUND: Venous thromboembolism (VTE) remains a critical cause of mortality among patients who are hospitalized. Patients with traumatic … ogistic regression (LR), random forest, gradient boosting decision tree, extreme gradient boosting, support vector machine, and categorical boosting, were trained and validated. Model performance was evaluated using receiver operating characteristic analysis, calibration curves (assessing probability-observation alignment), and decision curve analysis to quantify clinic …

Functional Connectivity Changes in Primary Motor Cortex Subregions of Patients With Obstructive Sleep Apnea.

BACKGROUND AND PURPOSE: Obstructive sleep apnea (OSA) is linked to cognitive impairment and altered motor-related brain networks. This study … linical variables in patients with OSA. Additionally, we employed three machine learning algorithms-support vector machine (SVM), random forest (RF), and logistic regression (LR)-to distinguish patients with OSA from HC based on FC features. RESULTS: Compared to HC, the OSA group found that significant FC enhancements were identified in right A6cdl with the left inferi …

Machine learning insights into patient satisfaction following lateral lumbar interbody fusion.

PURPOSE: Lateral Lumbar Interbody Fusion (LLIF) has become a minimally invasive procedure for treating degenerative lumbar conditions. While it offers reduced blood loss and faster recovery, patient satisfaction following LLIF surgery shows significant variability. Identifying the factors influencing satisfaction is crucial for optimizing surgical outcomes and improving patient care. This study aims to determine key factors affecting patient satisfaction after LLIF surgery using machine learning (ML) models, including Random Forest, Logistic Regression, Support Vector Machine (SVM), and k-Near…

Preoperative MRI-based radiomics analysis of intra- and peritumoral regions for predicting CD3 expression in early cervical cancer.

The study investigates the correlation between CD3 T-cell expression levels and cervical cancer (CC) while developing a magnetic resonance ( … and 5 mm margins (ROI3mm and ROI5mm, respectively). Various machine learning algorithms, including Support Vector Machine (SVM), Logistic Regression, Random Forest, AdaBoost, and Decision Tree, were used to construct radiomics models based on different ROIs, and diagnostic performances were compared to identify the optimal approach. The best-performing algorithm was co …

Effect and prediction of long-term weather and pollutant exposure on hemorrhagic fever with renal syndrome: based on statistical models.

BACKGROUND: Previous studies have typically explored daily lagged relationships between hemorrhagic fever with renal syndrome (HFRS) and met … showed a protective lag effect. The model of PM2.5 and the AQI interaction pollution is better. The support vector machine (SVM)-radial algorithm outperformed other algorithms when pollutants are used as predictor variables. CONCLUSION: This is the first mathematically based study of the seasonal threshold of HFRS in northeastern China, allowing for accurate estimation …

Повышение оптической согласованности диагностических возможностей: методы машинного обучения для выявления аутоиммунных воспаленных заболеваний.

БАКГРЕСС: Во многих частях мира, включая Индию, растет распространенность аутоиммунных подстрекательских заболеваний, таких, как расстройства спектрального спектра нейромиелита (NMOSD), гликопротеина (MOGAD) миэлина олигодендроцита, а также множественный склероз (MS). Диагноз часто задерживается из-за недостаточности диагностических средств. Модели машинного обучения (ML) позволяют точно дифференцировать глаза пациентов с MS от глаз здоровых органов управления (HC) с использованием оптических топографических изображений сетчатки (OCT). Изучение характеристик ОХТ может позволить провести раннюю

Совпадение в аннотации оригинала
BACKGROUND: In many parts of the world including India, the prevalence of autoimmune inflammatory diseases such as neuromyelitis optica spec … (OTHER, n = 16), and HCs (n = 54) from the Mangalore Demyelinating Disease Registry were included. Support vector machine (SVM) classification models incorporating age, pRNFL, GCIPL, and INL were performed. Data were split into training (70%) and testing (30%) data and accounted for within-patient correlations. Cross-validation was used in training to choose the best p …