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Predicting determinants of unimproved water supply in Ethiopia using machine learning analysis of EDHS-2019 data.

Over 2 billion people worldwide are impacted by the global dilemma of access to clean and safe drinking water. The problem is most acute in … his study. The following six machine-learning models were used: k-nearest Neighbors, Random Forest, Support Vector Machines, Gradient Boosting Machines, and Artificial Neural Networks. To enhance model performance and prevent overfitting, Hyperparameter adjustment was accomplished via random search and 7-fold cross-validation. The model's performance was evaluated using …

Predicting polycystic ovary syndrome with machine learning algorithms from electronic health records.

INTRODUCTION: Predictive models have been used to aid early diagnosis of PCOS, though existing models are based on small sample sizes and limited to fertility clinic populations. We built a predictive model using machine learning algorithms based on an outpatient population at risk for PCOS to predict risk and facilitate earlier diagnosis, particularly among those who meet diagnostic criteria but have not received a diagnosis. METHODS: This is a retrospective cohort study from a SafetyNet hospital's electronic health records (EHR) from 2003-2016. The study population included 30,601 women age

A task-aligned multimodal machine learning framework for studying working memory dysfunction in Parkinson's disease.

UNLABELLED: Cognitive impairment is one of the most functionally debilitating non-motor symptoms in Parkinson's disease (PD). Yet, current d … ures, which guided subsequent recursive elimination (RE) for computational efficiency. We trained a support vector machine with a radial basis function kernel (SVM-RBF) to classify PD and evaluated the final model's performance on a hold-out dataset. The automated elbow method identified optimal cutoffs at 20 PCs and 22 original features. The RE revealed that a model us …

Machine Learning-Directed Explainable Artificial Intelligence and Bayesian Predictions of Warfarin Therapeutic Dose for Orthopedic Indications.

INTRODUCTION: Warfarin, a cornerstone anticoagulant for preventing venous thromboembolism following orthopedic procedures such as total hip … ting (20%) sets. Five ML algorithms were implemented and compared: elastic net, random forest (RF), support vector machine (SVM), gradient boosting machine, and XGBoost. The following metrics were used for assessing the model performance: mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), R², and the percentage of predictio …

Opportunistic Osteoporosis Screening from Routine Knee Radiographs Using a Multi-Stage CNN Framework with External Validation.

Background/Objectives: Osteoporosis is a major public health concern associated with increased fracture risk and reduced quality of life if … ntly classified using multiple machine learning models, including Neural Network, Efficient Linear, Support Vector Machine, and Naive Bayes classifiers. Two data augmentation strategies were investigated: targeted minority-class augmentation and full 3× dataset expansion with class balancing. Model performance was evaluated using accuracy, precision, recall, F1-score, a …

Noncontrast MRI-based machine learning and radiomics signature can predict the severity of primary lower limb lymphedema.

OBJECTIVE: According to International Lymphology Society guidelines, the severity of lymphedema is determined by the difference in volume be … Using Scikit-learn, the remaining features were used to construct five models: logistic regression, support vector machine, random Forest, ExtraTrees, and light gradient boosting machine. The predictive performance were evaluated by the receiver operating characteristic curve, and the sensitivity and specificity of these measures were calculated. The predictive curve wa …

Gender-Driven English Speech Emotion Recognition with Genetic Algorithm.

Speech emotion recognition based on gender holds great importance for achieving more accurate, personalized, and empathetic interactions in … posed algorithm was compared with state-of-the-art algorithms on four common English datasets using support vector machines (SVM), and it demonstrated superior performance in accuracy, precision, recall, F1-score, the number of selected features, and running time. The proposed algorithm faced challenges in distinguishing between neutral, sad, and fearful emotions, due t …

Sanitary Waters: Is It Worth Looking for Mycobacteria?

The freshwater environment is suitable for nontuberculous mycobacteria (NTMs) growth. Their high adaptability represents a considerable risk for sanitary water systems, which are a potential vector for NTMs transmission. This study investigated the occurrence of NTMs, such as Mycobacterium saskatchewanense, in hospital water systems to support the surveillance and control of potentially pathogenic NTMs. We analyzed 722 ultrapure dialysis fluid samples from Emilia Romagna Dialysis Services. Among these, 35 samples were found to be positive for M. saskatchewanense. The strains were characterized

Disease-related compound identification based on deeping learning method.

Acute lung injury (ALI) is a serious respiratory disease, which can lead to acute respiratory failure or death. It is closely related to the … k (forgeNet) is proposed to train. The experimental results show that forgeNet performs better than support vector machines (SVM), random forest (RF), logical regression (LR), Naive Bayes (NB), XGBoost, LightGBM and gcForest. forgeNet could identify 19 compounds in Erhuang decoction (EhD) and Dexamethasone (DXMS) more accurately.

NRSN1 associated grey matter volume of the visual word form area reveals dyslexia before school.

Literacy learning depends on the flexibility of the human brain to reconfigure itself in response to environmental influences. At the same t … wed from kindergarten to first grade, were classified as dyslexics and controls using linear binary support vector machines. The NRSN1-associated grey matter volume of the 'visual word form area' achieved a classification accuracy of ~ 73% in literacy-experienced students and distinguished between later dyslexic individuals and controls with an accuracy of 75% at kinder …

Automated recognition of patients with obstructive sleep apnoea using wavelet-based features of electrocardiogram recordings.

Patients with obstructive sleep apnoea syndrome (OSAS) are at increased risk of developing hypertension and other cardiovascular diseases. This paper explores the use of support vector machines (SVMs) for automated recognition of patients with OSAS types (+/-) using features extracted from nocturnal ECG recordings, and compares its performance with other classifiers. Features extracted from wavelet decomposition of heart rate variability (HRV) and ECG-derived respiration (EDR) signals of whole records (30 learning sets from physionet) are presented as inputs to train the SVM classifier to reco…

Machine learning models to further identify advantaged populations that can achieve functional cure of chronic hepatitis B virus infection after receiving Peg-IFN alpha treatment.

OBJECTIVE: Functional cure is currently the highest goal of hepatitis B virus(HBV) treatment.Pegylated interferon(Peg-IFN) alpha is an impor … aset.Then,we employed six machine learning(ML) algorithms-Logistic Regression(LR),Random Forest(RF),Support Vector Machines(SVM),K Nearest Neighbors(KNN),Light Gradient Boosting Machine(LightGBM) and Extreme Gradient Boosting(XGBoost)-to develop the model.Internal 10-fold cross-validation helped determine the best-performing model,which was then tested externally.Model …

Post-concussive mTBI in Student Athletes: MRI Features and Machine Learning.

Purpose: To determine and characterize the radiomics features from structural MRI (MPRAGE) and Diffusion Tensor Imaging (DTI) associated wit … divided into five different data sets used to train and cross-validate five different filter-based Support Vector Machines. The top selected features of the top model were described. Furthermore, the test predictions of the top four models were ensembled into a single average prediction. The average prediction was evaluated for the association to the number of concussi …

AI-driven integration of Framingham Heart Study data with machine learning, deep learning, and explainable AI for enhanced pharmaceutical marketing.

AI algorithms, in drug discovery, support target identification by recognizing biological patterns and molecular interactions linked to dise … nt-centered practices. In this study, multiple machine learning and deep learning models, including support vector machine (SVM), random forest, XGBoost, logistic regression, feed forward neural network (FFNN), and multi-layer perceptron (MLP), were systematically evaluated for predicting cardiovascular disease outcomes using the Framingham Heart Study dataset. The SVM …

Machine Learning for Predicting Postoperative Functional Disability and Mortality Among Older Patients With Cancer: Retrospective Cohort Study.

BACKGROUND: The global cancer burden is rapidly increasing, with 20 million new cases estimated in 2022. The world population aged ≥65 years … ost), extreme gradient boosting (XGBoost), logistic regression, neural networks, random forest, and support vector machine. Model predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC) with 95% CI. We used the Shapley additive explanations (SHAP) method to evaluate contribution to the predictive performance for each p …

Количественное картирование T1 указывает на повышенный уровень белой материи миелин в детях с РАСопатией.

Факты говорят о патологической роли миэлирования в нейроразвитых расстройствах, связанных с когнитивными трудностями, но в vivo оценка остается сложной. Количественное картирование Т1 (QT1) использовалось в предыдущих клинических исследованиях (например, множественный склероз) и свидетельствует о перспективах надежного измерения изменений миелина. Мы изучили QT1 для измерения миэлизации у детей с расстройствами нейроразвитости белковой киназы рас/митогенов (RAS-MAPK), сигнализирующей траектории (RASopaties). МЕТОДЫ: Мы собрали QT1, взвешенные, и структурные магнитно-резонансные снимки с 72 дет

Совпадение в аннотации оригинала
BACKGROUND: Evidence suggests a pathological role of myelination in neurodevelopmental disorders with links to cognitive difficulties, but i … yelination patterns across 360 regions, followed by a multivariate analysis of variance (MANOVA). A support vector machine (SVM) identified the most discriminative features between groups. RESULTS: Of 39 tracts, 34 were higher in MTV in children with RASopathies relative to TD children (false discovery rate-corrected p < .05), indicating widespread elevation in myelina …

A deep learning model based on contrast-enhanced computed tomography for differential diagnosis of gallbladder carcinoma.

BACKGROUND: Gallbladder carcinoma (GBC) is highly malignant, and its early diagnosis remains difficult. This study aimed to develop a deep l … ng model was developed based on ResNet50 network. Radiomics and clinical models were built based on support vector machine (SVM) method. We comprehensively compared the performance of deep learning, radiomics, clinical models, and three radiologists. RESULTS: Three radiomics features including LoG_3.0 gray-level size zone matrix zone variance, HHL first-order kurtosis, …

Value of Ultrasonic Image Features in Diagnosis of Perinatal Outcomes of Severe Preeclampsia on account of Deep Learning Algorithm.

This study is aimed at discussing the value of ultrasonic image features in diagnosis of perinatal outcomes of severe preeclampsia on account of deep learning algorithm. 140 pregnant women singleton with severe preeclampsia were selected as the observation group. At the same time, 140 normal singleton pregnant women were selected as the control group. The hemodynamic indexes were detected by color Doppler ultrasound. The CNN algorithm was used to classify ultrasound images of two groups of pregnant women. The differential scanning calorimetry (DSC), mean pixel accuracy (MPA), and mean intersec

Incremental value of biomarker combinations to predict progression of mild cognitive impairment to Alzheimer's dementia.

BACKGROUND: The progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) dementia can be predicted by cognitive, neuroimag … arameter combinations were analyzed for significant superiority by a bootstrapping wrapper around a support vector machine with linear kernel. The incremental value of combinations was tested for statistical significance by comparing the specificities of the different classifiers at a given sensitivity of 85%. RESULTS: Out of 115 subjects, 28 (24.3%) with MCI progresse …

Identification of Amnestic Mild Cognitive Impairment Using Multi-Modal Brain Features: A Combined Structural MRI and Diffusion Tensor Imaging Study.

Identifying amnestic mild cognitive impairment (aMCI) is of great clinical importance because aMCI is a putative prodromal stage of Alzheime … acted from a relatively large sample of 64 Han Chinese aMCI patients and 64 matched controls. Then, support vector machine classifiers for GM volume, FA, and MD were fused to distinguish the aMCI patients from the controls. The fused classifier was evaluated with the leave-one-out and the 10-fold cross-validations, and the classifier had an accuracy of 83.59% and an are …