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Associations of the NPAR index with breast cancer incidence and mortality based on the National Health and Nutrition Examination Survey (NHANES) 2001-2018: a cross-sectional study.

BACKGROUND: Breast cancer (BC) remains one of the most prevalent cancers affecting women globally, imposing significant health and economic … ming models such as CATBoost, Naive Bayes, logistic regression, random forest, K-nearest neighbors, support vector machine, decision tree, and XGBoost. After model selection, an online calculator was built for use in the clinic, and the web-service is available at https://fast.statsape.com/tool/detail?id=11 . CONCLUSION: NPAR emerged as a crucial biomarker in BC risk a …

Machine Learning Prediction of Short Cervix in Mid-Pregnancy Based on Multimodal Data from the First-Trimester Screening Period: An Observational Study in a High-Risk Population.

Background: A short cervix in the second trimester significantly increases preterm birth risk, yet no reliable first-trimester prediction me … en machine learning models (logistic regression, linear discriminant analysis, k-nearest neighbors, support vector machine, decision tree, random forest, XGBoost) were trained to predict mid-trimester short cervix. The XGBoost model-an ensemble method leveraging sequential decision trees-was analyzed using Shapley Additive Explanation (SHAP) values to assess feature imp …

From gaze to proficiency: deep learning-driven prediction of novice performance in laparoscopic training using AOI-dependent metrics.

BACKGROUND: The fundamentals of laparoscopic surgery (FLS) program uses box trainers to develop laparoscopic skills. However, these simulato … s' visual behavior, we employed multiple Machine Learning (ML) techniques, including Random Forest, Support Vector Machine, Artificial Neural Networks, and Decision Trees. These methods were used to evaluate which technique could most accurately predict trainees' visual attention patterns. RESULTS: The extracted metrics successfully classified novices into High and Mid …

Using Machine Learning to Predict Medication Therapy Problems among Patients with Chronic Kidney Disease.

INTRODUCTION: Patients with chronic kidney disease (CKD) are at risk of medication therapy problems (MTPs) due to high comorbidity and medic … ut MTP. Eight candidate models were considered, and the top three performing models (random forest, support vector machines, and gradient boosting), based on cross-validated AUROC on training data, underwent further refinement. The model with the highest AUROC in the testing set, while considering the bias/variance trade-off, was selected as the best-performing model. S …

In-depth bioinformatics analysis uncovers the crosstalk genes and immune interactions among diagnostic markers linked to natural killer cells in patients with cirrhosis and sepsis.

Patients with cirrhosis face an elevated risk of developing sepsis, leading to an escalating mortality rate. This study focuses on the link … rentially expressed genes (DEGs), sepsis DEGs, and key genes identified by WGCNA and screened using support vector machine-recursive feature elimination (SVM-RFE) and Random Forest machine learning algorithms. To rigorously validate the diagnostic significance of the feature genes, we performed receiver operating characteristic (ROC) curve and decision curve analysis (D …

The association between social factors and migraine or severe headache in men: a secondary analysis of national population-based studies with machine learning models.

BACKGROUND: Migraine is recognized as a global public health issue. It is more prevalent among women; therefore, studies focusing solely on … ormance of six machine learning models: XGBoost, Random Forest, Decision Tree, Logistic Regression, Support Vector Machine, and K-Nearest Neighbors. RESULTS: Of 2,496 male Chinese participants, 236 (9.5%) were diagnosed with migraine or severe headache. According to the NHANES data, 714 (16.6%) of 4,303 male participants reported migraine or severe headache. Logistic r …

Machine Learning-Based Identification of Blood Biomarkers that Distinguish Precachectic and Cachectic Patients with Pancreatic Ductal Adenocarcinoma.

BACKGROUND: Identification of minimally invasive biomarkers of different stages of cachexia (Ca), and precachexia (PCa) in particular, might … orward feature selection method; (iii) determine the optimal classification hyperparameters for the support vector machine using a cross-validation technique; and (iv) adjust the decision-boundary threshold for imbalanced data using the Matthews correlation coefficient. Three ML-based binary predictors were designed to determine patients' cachexia status: NCa vs. Ca; PC …

Machine Learning Prediction for Spinal Deformity Surgery Blood Transfusion.

BACKGROUND: Spinal deformity surgery (SDS) is usually accompanied by significant intraoperative blood loss and transfusion, which is not wit … r, neural networks, Light Gradient Boosting Machine, random forest (RF), eXtreme Gradient Boosting, support vector machine, and stacking ensemble model. The performance of these models was evaluated using receiver operating characteristic (ROC) curve, precision-recall, calibration, and decision curve analysis. In addition, SHapley Additive exPlanation was applied to int …

Machine learning-based prediction model for patients with recurrent Staphylococcus aureus bacteremia.

BACKGROUND: Staphylococcus aureus bacteremia (SAB) remains a significant contributor to both community-acquired and healthcare-associated bl … were built using Extreme Gradient Boosting (XGBoost), Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Artificial Neural Network (ANN). Model validation included Receiver Operating Characteristic (ROC) analysis, Decision Curve Analysis (DCA), and Precision-Recall Curve (PRC). We utilized SHAP (SHapley Additive exPlanations) values to demon …

Менделийская случайность и одноклеточный стеноскопический анализ свидетельствуют о том, что серотонин способствует множеству склерозов путем подавления активности аденозина деаминазы.

ЦЕЛЬ: Изучение причинно-следственной связи между уровнями серотонина, активностью аденозина деаминазы (АДА) и развитием множественных склерозов (МС) с использованием комплексного подхода, предусматривающего использование мультиомики. МЕТОДЫ И АНАЛИЗ: Для оценки причинно-следственной связи между риском серотонина, АДА и СС был проведен двухвыборный анализ случайности (MR) с использованием взвешенной оценки обратной вариации (IVW). Для выявления подпопуляций, прямо выраженных в ADA, были проанализированы данные с одноклеточной транскрипцией, полученные из " Gene Express Omnibus " (GSE1

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OBJECTIVE: To investigate the causal relationship between serotonin levels, adenosine deaminase (ADA) activity and multiple sclerosis (MS) p … lysed to identify ADA-expressing immune cell subpopulations. Moreover, machine learning algorithms (Support Vector Machine-Recursive Feature Elimination, Least Absolute Shrinkage and Selection Operator and random forest) were applied to identify diagnostic biomarkers, following which a nomogram was constructed and validated. RESULTS: MR analysis revealed that serotonin …

Predicting early stage lung cancer recurrence and survival from combined tumor motion amplitude and radiomics on free-breathing 4D-CT.

BACKGROUND: Cancer control outcomes of lung cancer are hypothesized to be affected by several confounding factors, including tumor heterogen … o predict the overall outcomes, which was compared to three other approaches - logistic regression, support vector machine, and convolutional neural networks. RESULTS: A 6-fold cross-validation study yielded an area under the receiver operating characteristic curve of 72% for progression-free survival when combining clinical data with radiomics features and tumor motio …

Predicting the occurrence of mild cognitive impairment in Parkinson's disease using structural MRI data.

INTRODUCTION: Mild cognitive impairment (MCI) is a common symptom observed in individuals with Parkinson's disease (PD) and a main risk fact … linical characteristics with PD-UHC at baseline. Seventy-five percent of these were used to train a support vector machine (SVM) algorithm to distinguish between the PD-UHC and PD-SHC groups, and tested on the remaining 25% of individuals. Shapley Additive Explanations (SHAP) feature analysis was utilized to identify the most informative brain regions in SVM classifier. …

Applications of machine learning in pediatric traumatic brain injury (pTBI): a systematic review of the literature.

OBJECTIVE: Pediatric traumatic brain injury (pTBI) is a heterogeneous condition requiring the development of clinical decision rules (CDRs) … the maximum accuracy to predict the outcome (favorable vs. unfavorable) of patients with TBI was a support vector machine (SVM) model with 94.0% accuracy, whereas the model with the highest AUC was an ANN model with an AUC of 0.991. CONCLUSION: In the present systematic review, conventional and novel ML models were utilized to either predict the presence of intracrani …

Classification of vasovagal syncope from physiological signals on tilt table testing.

BACKGROUND: The diagnostic test for vasovagal syncope (VVS), the most common cause of syncope is head-up tilt test (HUTT) assessment. During … ference between two groups. Patients with VVS are categorized via machine learning models including Support Vector Machine (SVM), Gaussian Naïve Bayes (GNB), Multinomial Naïve Bayes (MNB), KNN, Logistic Regression (LR), and Random Forest (RF). The developed model is interpreted using an explainable artificial intelligence (XAI) model known as partial dependence plot. R …

Enhanced osteoporotic fracture prediction in postmenopausal women using Bayesian optimization of machine learning models with genetic risk score.

This study aimed to enhance the fracture risk prediction accuracy in major osteoporotic fractures (MOFs) and hip fractures (HFs) by integrating genetic profiles, machine learning (ML) techniques, and Bayesian optimization. The genetic risk score (GRS), derived from 1,103 risk single nucleotide polymorphisms (SNPs) from genome-wide association studies (GWAS), was formulated for 25,772 postmenopausal women from the Women's Health Initiative dataset. We developed four ML models: Support Vector Machine (SVM), Random Forest, XGBoost, and Artificial Neural Network (ANN) for binary fracture outcome a…

Combined transcriptomics and proteomics forecast analysis for potential biomarker in the acute phase of temporal lobe epilepsy.

BACKGROUND: Temporal lobe epilepsy (TLE) is a common chronic episodic illness of the nervous system. However, the precise mechanisms of dysf … alysis of DEPs and DEGs. The least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE) algorithms were used to screen Hub genes in the acute phase of TLE, and logistic regression algorithms were applied to develop a novel diagnostic model for the acute phase of TLE, and the sensitivity of the di …

Machine learning prediction model for the rupture status of middle cerebral artery aneurysm in patients with hypertension: a Chinese multicenter study.

BACKGROUND: Hypertension is a common comorbidity in patients with unruptured intracranial aneurysms and is closely associated with the ruptu … SULTS: The areas under the ROC curve (AUCs) of the k-nearest-neighbor (KNN), neural network (NNet), support vector machine (SVM) and logistic regression (LR) models in the training datasets were 0.83 [95% confidence interval (CI): 0.78-0.88], 0.87 (95% CI: 0.82-0.92), 0.91 (95% CI: 0.88-0.95), and 0.83 (95% CI: 0.77-0.88), respectively, and in the testing datasets were …

Amygdala's T1-weighted image radiomics outperforms volume for differentiation of anxiety disorder and its subtype.

INTRODUCTION: Anxiety disorder is the most common psychiatric disorder among adolescents, with generalized anxiety disorder (GAD) being a co … rmed group-wise comparisons, and use different machine learning algorithms, including linear kernel support vector machine (SVM), to achieve the classification between the patients and healthy controls. RESULTS: For the classification task of anxiety patients vs. healthy controls, 2 and 4 radiomics features were selected from left and right amygdala, respectively, and …

Identification of Biomarkers Associated with Heart Failure Caused by Idiopathic Dilated Cardiomyopathy Using WGCNA and Machine Learning Algorithms.

BACKGROUND: The genetic factors and pathogenesis of idiopathic dilated cardiomyopathy-induced heart failure (IDCM-HF) have not been understo … ed by intersecting the key module genes identified via WGCNA with DEGs and further screened via the support vector machine-recursive feature elimination (SVM-RFE) method and the least absolute shrinkage and selection operator (LASSO) algorithm. At last, the biomarkers were validated and evaluated the diagnostic efficacy by the area under curve (AUC) value and further co …

Predicting transient ischemic attack risk in patients with mild carotid stenosis using machine learning and CT radiomics.

OBJECTIVE: This study aims to establish a radiomics-based machine learning model that predicts the risk of transient ischemic attack in pati … tion algorithms were used, including random forest, eXtreme Gradient Boosting, logistic regression, support vector machine, and k-nearest neighbors. Data on radiomic feature information, clinical information, and the combination of these pieces of information were used to generate the model that predicts the risk of transient ischemic attack in patients with mild caroti …