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Development of a machine learning-based model to predict prognosis of alpha-fetoprotein-positive hepatocellular carcinoma.

BACKGROUND: Patients with alpha-fetoprotein (AFP)-positive hepatocellular carcinoma (HCC) have aggressive biological behavior and poor progn … Results database. Six ML algorithms (extreme gradient boosting [XGBoost], logistic regression [LR], support vector machine [SVM], random forest [RF], K-nearest neighbor [KNN], and decision tree [ID3]) were used to develop the prognostic models of patients with AFP-positive HCC at one year, three years, and five years. Area under the receiver operating characteristic cur …

On relevant features for the recurrence prediction of urothelial carcinoma of the bladder.

BACKGROUND: Urothelial bladder cancer (UBC) is characterized by a high recurrence rate, which is predicted by scoring systems. However, rece … es of 1,944 patients with UBC. We retrospectively predict 2-year recurrence through ML models using Support Vector Machine, Gradient Boosting, and Artificial Neural Network. We then determine the global feature importance using performance-based Permutation Feature Importance (PFI) and variance-based Feature Importance Ranking Measure (FIRM). RESULTS: We show reliable …

Analyzing the Prevalence of Depression and Its Influencing Factors in Elderly Patients With Obstructive Sleep Apnea: A Machine Learning Approach.

Objective: Depressive symptoms are prevalent and detrimental in elderly patients with obstructive sleep apnea (OSA). Understanding the facto … tors, and random forest (RF), extreme gradient boosting (XGB), artificial neural network (ANN), and support vector machine (SVM) were utilized to 4 algorithms were used to construct depressive symptoms classification models, and the best model performance was selected for feature importance ranking. Influential factors included demographics (age, gender, education, etc. …

Identification of immune-related mitochondrial metabolic disorder genes in septic shock using bioinformatics and machine learning.

PURPOSE: Mitochondria are involved in septic shock and inflammatory response syndrome, which severely affects the life security of patients. … The Protein-Protein Interaction (PPI) network composed of MitoDEGs was established using Cytoscape. Support Vector Machine Recursive Feature Elimination (SVM-RFE), Random Forest (RF), and Least Absolute Shrinkage and Selection Operator (LASSO) were used to identify diagnostic MitoDEGs, which were validated using receiver operating characteristic (ROC) analysis and Quant …

Community screening for dementia among older adults in China: a machine learning-based strategy.

BACKGROUND: Dementia is a leading cause of disability in people older than 65 years worldwide. However, diagnosing dementia in its earliest … ance was used to select features. Five ML models using BalanceCascade were applied to predict CI: a support vector machine (SVM), multilayer perceptron (MLP), AdaBoost, gradient boosting decision tree (GBDT), and logistic regression (LR). An AD8 score ≥ 2 was used to define CI as a baseline. SHapley Additive exPlanations (SHAP) values were used to interpret the results …

Combining static and dynamic functional connectivity analyses to identify male patients with obstructive sleep apnea and predict clinical symptoms.

BACKGROUND AND PURPOSE: Patients with obstructive sleep apnea (OSA) experience chronic intermittent hypoxia and sleep fragmentation, leading … gyrus. Combined dynamic-static FC features demonstrated superior classification performance with a support vector machine in discriminating OSA from HC. In clinical symptom prediction, FC alterations contributed up to 30.11 % to cognitive impairment, 55.96 % to excessive sleepiness, and 27.94 % to anxiety and depression. CONCLUSIONS: Combining cerebrocerebellar sFC an …

Deep Learning Based Micro-RNA Analysis of Lipopolysaccharide Exposed Periodontal Ligament Stem Cells Exosomes Reveal Apoptotic and Inflammasome Derived Pathway Activation.

BACKGROUND: The production of inflammatory factors in periodontium is increased by LPS, particularly from P. gingivalis, and the damage to p … uced PDLSC cells. Deep learning analysis, employing a Random Forest, Artificial Neural Network c, a Support Vector Machine (SVM), and a Linear Regression model implemented within the orange data mining toolkit, identified novel microRNA biomarkers. The orange data mining toolkit was utilized for deep learning analysis of microRNA expression data, providing a user-friend …

Developing Machine Learning-Based Predictive Models for Hallux Valgus Recurrence Based on Measurements From Radiographs.

BACKGROUND: Machine learning (ML) is increasingly used to predict the prognosis of numerous diseases. This retrospective analysis aimed to d … specificity of each model were calculated to evaluate the performance of each model. RESULTS: The support vector machine (SVM) model showed the highest predictive accuracy compared to other methods, with an AUC of 0.88 and an accuracy of 75.6%. Preoperative hallux valgus angle, tibial sesamoid position, postoperative intermetatarsal angle, and postoperative tibial ses …

Differentiation of benign and malignant parotid gland tumors based on the fusion of radiomics and deep learning features on ultrasound images.

OBJECTIVE: The pathological classification and imaging manifestation of parotid gland tumors are complex, while accurate preoperative identi … followed by feature fusion. Seven machine learning classifiers including logistic regression (LR), support vector machine (SVM), RandomForest, ExtraTrees, XGBoost, LightGBM and multi-layer perceptron (MLP) were combined to construct predictive models. The most optimal model was integrated with clinical and US features to develop a nomogram. Receiver operating character …

Биоинформатика и экспериментальная проверка сочетались для изучения биомаркеров, связанных с лактилацией, в случае острой недостаточности печени, связанной с ГБТС.

В настоящее время острая недостаточность печени, вызванная вирусом гепатита В (HBV-ALF), имеет ограниченные возможности для лечения. Исследования показали, что гистонная лактиляция играет определенную роль в развитии болезней, связанных с печенью. Поэтому для получения новой информации для лечения ГБВ-АФМ необходимо исследовать биомаркеры, связанные с лактилацией (ЛРГ), в HBV-ALF. Были использованы два набора данных, связанных с ГБТС-АФО (GSE38941 и GSE14668), и 65 ЛРГ. Во-первых, дифференциально выраженные гены (DEGs) были получены на основе дифференциального экспресс-анализа, ключевые модуль

Совпадение в аннотации оригинала
BACKGROUND AND AIM: Currently, hepatitis B virus-related acute liver failure (HBV-ALF) has limited treatment options. Studies have shown tha … feature genes obtained from least absolute shrinkage and selection operator regression analysis and support vector machine analysis were intersected to obtain the candidate key genes. Among them, genes with consistent and significant expression trends in both GSE38941 and GSE14668 were used as biomarkers. Subsequently, biomarkers were analyzed for functional enrichment, …

Exploring the Feasibility of Machine Learning to Predict Risk Stratification Within 3 Months in Chest Pain Patients with Suspected NSTE-ACS.

OBJECTIVE: We aimed to assess the feasibility and superiority of machine learning (ML) methods to predict the risk of Major Adverse Cardiova … d better than HEART scoring system only in prediction for AMI. Among the five ML algorithms, Linear support vector machine (SVC), Naïve Bayes and Logistic regression classifiers stood out with all Accuracy, Precision, Recall and F-Measure from 0.8 to 1.0 for predicting any event, AMI, revascularization and all-cause death ( vs. HEART ≤ 0.78), with AUC from 0.88 to 0.98 …

Prediction of non-perfusion volume ratio for uterine fibroids treated with ultrasound-guided high-intensity focused ultrasound based on MRI radiomics combined with clinical parameters.

BACKGROUND: Prediction of non-perfusion volume ratio (NPVR) is critical in selecting patients with uterine fibroids who will potentially ben … selection operator (LASSO) algorithm was employed for feature selection in the training cohort. The support vector machine (SVM) was adopted to construct a radiomics model, a clinical model, and a radiomics-clinical model for NPVR prediction, respectively. The area under the curve (AUC) and the decision curve analysis (DCA) were performed to evaluate the predictive vali …

Application of Interpretable Machine Learning Models Based on Ultrasonic Radiomics for Predicting the Risk of Fibrosis Progression in Diabetic Patients with Nonalcoholic Fatty Liver Disease.

INTRODUCTION: Patients with nonalcoholic fatty liver disease (NAFLD) and type 2 diabetes mellitus (T2DM) face a significant risk of hepatic … hepatic fibrosis. The study identified 25 significant fibrosis-related radiomics features, with the support vector machine (SVM) model demonstrating superior performance in both internal and external validations. The SHAP analysis identified five key determinants of fibrosis risk, which included three radiomics features from shear wave elastography (SWE) and two from gr …

Machine Learning Approaches with Textural Features to Calculate Breast Density on Mammography.

BACKGROUND: breast cancer (BC) is the world's most prevalent cancer in the female population, with 2.3 million new cases diagnosed worldwide … f different tested classifiers varied between 74.15% and 93.55%. The best results were reached by a Support Vector Machine (accuracy of 93.55% and a percentage of true positives and negatives equal to TPP = 94.44% and TNP = 92.31%). The best accuracy was not influenced by the choice of the features selection approach. Considering the external validation cohort, the SVM, …

Predicting case difficulty in endodontic microsurgery using machine learning algorithms.

OBJECTIVES: The study aimed to develop and validate machine learning models for case difficulty prediction in endodontic microsurgery, assisting clinicians in preoperative analysis. METHODS: The cone-beam computed tomographic images were collected from 261 patients with 341 teeth and used for radiographic examination and measurement. Through linear regression (LR), support vector regression (SVR), and extreme gradient boosting (XGBoost) algorithms, four models were established according to different loss functions, including the L1-loss LR model, L2-loss LR model, SVR model and XGBoost model.

Longitudinal real world correlation study of blood pressure and novel features of cerebral magnetic resonance angiography by artificial intelligence analysis on elderly cognitive impairment.

OBJECTIVE: This study aims to investigate novel clinical risk factors for cognitive impairment (CI) in elderly. METHODS: A total of 3221 pa … and CI. Four machine learning approaches, including LR, decision tree (DT), random forest (RF), and support vector machine (SVM), employing 5-fold cross-validation were used to establish CI predictive models. RESULTS: After matching with age and gender, 208 CI patients and 208 control subjects were finalized the follow-up (3.46 ± 3.19 years) with mean age at 84.47 ± 6. …

Prediction of the mean transit time using machine learning models based on radiomics features from digital subtraction angiography in moyamoya disease or moyamoya syndrome-a development and validation model study.

BACKGROUND: Digital subtraction angiography (DSA) is an important technique for diagnosis of moyamoya disease (MMD) or moyamoya syndrome (MM … rator (LASSO) regression algorithm. Prediction models were established using random forest (RF) and support vector machine (SVM) for MTT values. Single-factor receiver operating characteristic (ROC) curve analysis and partial-dependence (PD) profiles were conducted to investigate selected features and prediction models. RESULTS: Our results showed that prediction model …

Machine learning-based approach for predicting low birth weight.

BACKGROUND: Low birth weight (LBW) has been linked to infant mortality. Predicting LBW is a valuable preventative tool and predictor of newb … lassification, deep learning feedforward, extreme gradient boost model, light gradient boost model, support vector machine, and permutation feature classification with k-nearest neighbors). Expert opinion and prior observational cohorts were used to select candidate LBW predictors for all models. The area under the receiver operating characteristic curve (AUROC), accura …

A machine learning approach for predicting descending thoracic aortic diameter.

BACKGROUND: To establish models for predicting descending thoracic aortic diameters and provide evidence for selecting the size of the stent … s at three predicted points were established using four algorithms included linear regression (LR), support vector machine (SVM), Extra-Tree regression (ETR) and random forest regression (RFR). The performance of models was evaluated by mean square error (MSE) of the prediction value, and the ranking of feature importance was given by Shapley value. After modeling, prog …

Identifying the potential transcriptional regulatory network in Hirschsprung disease by integrated analysis of microarray datasets.

OBJECTIVE: Hirschsprung disease (HSCR) is one of the common neurocristopathies in children, which is associated with at least 20 genes and i … s determined and the integrated diagnostic models were established based on machine learning by the support vector machine method. RESULTS: We identified 58 hub differentially expressed microRNAs (DEMis) and 16 differentially expressed mRNAs (DEMs). The robust target genes of DEMis and DEMs mainly enriched in several GO/KEGG terms, including neurogenesis, cell-substrat …