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An Optimal Approach for Heart Sound Classification Using Grid Search in Hyperparameter Optimization of Machine Learning.

Heart-sound auscultation is one of the most widely used approaches for detecting cardiovascular disorders. Diagnosing abnormalities of heart … ithms including k-nearest neighbor (K-NN), random forest (RF), artificial neural network (ANN), and support vector machine (SVM). The five-fold cross-validation was used to evaluate the performance of the proposed method. The best model obtained classification accuracy of 95.78% and 76.31%, which was assessed using PhysioNet Challenge 2016 and 2022, respectively. The fi …

Objective Recognition of Tinnitus Location Using Electroencephalography Connectivity Features.

Purpose: Tinnitus is a common but obscure auditory disease to be studied. This study will determine whether the connectivity features in ele … main features in the EEG signals were extracted, and four machine learning algorithms, included two support vector machine models (SVM), a multi-layer perception network (MLP) and a convolutional neural network (CNN), were used based on the selected features to classify different possible tinnitus sources. Results: Classification accuracy was highest when the SVM algori …

Identification and Classification of Parkinsonian and Essential Tremors for Diagnosis Using Machine Learning Algorithms.

Due to overlapping tremor features, the medical diagnosis of Parkinson's disease (PD) and essential tremor (ET) mainly relies on the clinical experience of doctors, which often leads to misdiagnosis. Seven predictive models using machine learning algorithms including random forest (RF), eXtreme Gradient Boosting (XGBoost), support vector machine (SVM), logistic regression (LR), ridge classification (Ridge), backpropagation neural network (BP), and convolutional neural network (CNN) were evaluated and compared aiming to better differentiate between PD and ET by using accessible demographics and…

Multi-Dimensional and Objective Assessment of Motion Sickness Susceptibility Based on Machine Learning.

BACKGROUND: As human transportation, recreation, and production methods change, the impact of motion sickness (MS) on humans is becoming mor … mus are related to the severity of MS. Among the ML assessment models based on these variables, the support vector machine classifier had the best performance with an accuracy of 88.24%, sensitivity of 91.43%, and specificity of 81.25%. CONCLUSION: The severity of symptoms and signs of MS can be objectively quantified using some indicators. Multi-dimensional and object …

Deep learning and radiomics based automatic diagnosis of hippocampal sclerosis.

Accurate and rapid segmentation of the hippocampus can help doctors perform intractable temporal lobe epilepsy (TLE) preoperative evaluation … of the segmented hippocampus. The iterative sparse representation based on feature selection and a support vector machine classifier were finally used to establish the diagnostic model of hippocampal sclerosis. The diagnostic model consists of two consecutive steps: distinguish hippocampal sclerosis (HS) from normal control (NC) and detect whether the HS is located on …

iPMI: Machine Learning-Aided Identification of Parametrial Invasion in Women with Early-Stage Cervical Cancer.

Radical hysterectomy is a recommended treatment for early-stage cervical cancer. However, the procedure is associated with significant morbi … (e.g., logistic regression, decision tree, k-nearest neighbor, multi-layer perceptron, naive Bayes, support vector machine, and extreme gradient boosting). Upon comparison, it was found that iPMI-Power was effective and had a superior performance to other well-known ML classifiers in predicting PMI. It is anticipated that the proposed iPMI may serve as a cost-effective …

Data-driven supervised learning of a viral protease specificity landscape from deep sequencing and molecular simulations.

Biophysical interactions between proteins and peptides are key determinants of molecular recognition specificity landscapes. However, an und … subset of experimentally derived sequences were used in a supervised learning procedure to train a support vector machine to predict the cleavability of 3.2 million substrate variants by the HCV protease. The resulting landscape allows identification of previously unidentified HCV protease substrates, and graph-theoretic analyses reveal extensive clustering of cleavabl …

Frontotemporal correlates of impulsivity and machine learning in retired professional athletes with a history of multiple concussions.

The frontotemporal cortical network is associated with behaviours such as impulsivity and aggression. The health of the uncinate fasciculus … pathological outcomes of repeated concussion as they relate to impulsive behaviour. Furthermore, a support vector machine has potential utility in the general assessment and diagnosis of brain abnormalities following concussion.

Prognosis-determinant candidate genes identified by whole genome scanning in eyes with pterygia.

PURPOSE: To identify the genes that can differentiate primary from recurrent pterygia. METHODS: The transcriptional differences of primary and recurrent pterygia were first determined by microarray analyses. Computational analyses were used to extract the biological significance of the genes accurately, and a significant functional classification of the genes was made by unsupervised methodologies. After confirming the functional classification for primary and recurrent pterygia by a clustering algorithm, a support vector machine (SVM) algorithm was applied. Based on a machine learning techni…

Celiac disease diagnosis from videocapsule endoscopy images with residual learning and deep feature extraction.

BACKGROUND AND OBJECTIVE: Videocapsule endoscopy (VCE) is a relatively new technique for evaluating the presence of villous atrophy in celia … re points from training and test samples before the last fully connected layer, and then input to a support vector machine (SVM), k-nearest neighbor (KNN), and linear discriminant analysis (LDA) for differentiating celiac disease images from heathy controls. RESULTS: Overall, the accuracy, sensitivity and specificity of the 10-time 10-fold cross-validation were 95.94%, …

Risk stratification with explainable machine learning for 30-day procedure-related mortality and 30-day unplanned readmission in patients with peripheral arterial disease.

Predicting 30-day procedure-related mortality risk and 30-day unplanned readmission in patients undergoing lower extremity endovascular inte … database. For each outcome, we developed and evaluated multiple machine learning models, including Support Vector Machines, Multilayer Perceptrons, and Gradient Boosting Machines, and selected a random forest as the best-performing model for both outcomes. Our 30-day procedure-related mortality model achieved an AUC of 0.75 (95% CI: 0.71-0.79) and our 30-day unplanned …

Early prediction of immune checkpoint inhibitor-related pneumonitis in advanced non-small cell lung cancer based on primary tumor delta-radiomics features.

OBJECTIVE: To investigate the effectiveness of predicting immune checkpoint inhibitor-related pneumonitis (ICIP) in patients with advanced n … ction Operator (LASSO) regression. Prediction models were developed using Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGBoost) algorithms. These models were compared and further integrated with a clinical feature-based model incorporating a history of interstitial lung disease, absolute lymphocyte cou …

Longitudinal observation of functional connectivity in the insular subregions, as a characteristic change for bipolar disorder.

BACKGROUND: The structural changes of the insula have been confirmed to be a stable and characteristic change in bipolar disorder (BD). More … BD patients before and after three-month treatment. The insular subregion FC was then used to train support vector machine (SVM) models for patient identification. RESULTS: At baseline, six insular subregions showed abnormal FC with the dorsolateral prefrontal cortex, orbitofrontal cortex, thalamus, and brain regions in the default mode network (DMN) and visual network …

Reconstruction of three human lymphocyte subtypes for benchmarking in 3D morphology and modeling.

BACKGROUND AND OBJECTIVE: Lymphocytes play critical roles in human immune response. Reconstruction of human primary cells from confocal imag … between CD4+ T and CD19+ B cells. The subtypes were classified by morphological parameters using a support vector machine method with accuracies much less than those by diffraction images. To illustrate the difference, we derived realistic optical cell models from the reconstructed lymphocytes to demonstrate that varied refractive index within organelles can supply int …

Redefining diagnostic lesional status in temporal lobe epilepsy with artificial intelligence.

Despite decades of advancements in diagnostic MRI, 30%-50% of temporal lobe epilepsy (TLE) patients remain categorized as 'non-lesional' (i. … ferentiate TLE from healthy controls with high accuracy (85.9% ± 2.8%), significantly outperforming support vector machines based on hippocampal (74.4% ± 2.6%) and whole-brain (78.3% ± 3.3%) volumes. Our analysis focused subsequently on a subset of patients who achieved sustained seizure freedom post-surgery as a gold standard for confirming TLE. Importantly, MRI-negati …

Cerebral microstructural alterations as an imaging biomarker for Post-COVID-condition.

To develop an imaging biomarker-based approach for the diagnosis of Post-COVID-condition (PCC) at the individual patient level. Magnetic res … re extracted using an atlas-based approach. These data were subsequently utilized to train a linear support vector machine (SVM). The efficacy of discrimination between the groups was evaluated for various combinations of input parameters. Upon comparison of the different input combinations, we found the highest area under the receiver operating characteristic curve (AU …

Prediction of T2/T3 Staging in Patients with Volume-Equivalent Esophageal Squamous Cell Carcinoma on the Basis of PET/CT Radiomics.

OBJECTIVES: To develop and externally validate PET/CT-based radiomic models for predicting tumor invasion depth (≤T2 vs. ≥T3) in patients wi … feature selection via LASSO and MRMR. Logistic regression (LR), linear discriminant analysis (LDA), support vector machine (SVM), and random forest (RF) models were constructed. Model performance was assessed on internal and independent external cohorts. RESULTS: In the internal validation, the PET and combined PET/CT radiomic models outperformed the CT-alone models, w …

Identification and characterization of cephalosporin-resistant Klebsiella pneumoniae by SERS: Insights into antimicrobial resistance patterns.

The infectious bacteria, K. pneumoniae has broken another defence line by showing resistance to cephalosporin antibiotics, due to presence o … neration cephalosporins, were differentiated by analyzing their SERS spectral data with the help of support vector machines (SVM), principal component analysis (PCA), and partial least squares discriminant analysis (PLS-DA). This can result in the effective recognition, differentiation and distinction of third generation cephalosporin resistant and susceptible strains o …

Utility of an artificial intelligence-based lung CT airway model in the quantitative evaluation of large and small airway lesions in patients with chronic obstructive pulmonary disease.

BACKGROUND: Accurate quantification of the extent of bronchial damage across various airway levels in chronic obstructive pulmonary disease … (CNR) and Airway Transfer Network (ATN) algorithms. The efficacy of the model was evaluated through support vector machine (SVM) and random forest regression approaches. RESULTS: The area under the receiver operating characteristic (ROC) curve (AUC) of the SVM in evaluating the COPD airway segmentation model was 0.96, with a sensitivity of 97% and a specificity of 92%, …

Прогнозирующая модель для пациентов с тройным отрицательным оптическим невритом: выявление подгруппы пациентов с благоприятными результатами.

Подгруппа оптического неврита (ON) остается серонегиозной для AQP4-IgG, MOG-IgG и CSF oligoclonal (OCBs), что осложняет диагностику и управление. Цель этого исследования состояла в том, чтобы охарактеризовать эти случаи с тройной отрицательной стороны (ТНК) и сравнить их с MS, MOGAD и NMOSD. Мы ретроспективно проанализировали 103 пациента с первым эпизодом AQP4-IgG, MOG-IgG и OCB, классифицировали их в MS (n=26), MOGAD (n=25), NMOSD (n=13) и TNON (n=39). Кроме того, TNON был разделен на развивающиеся MS (EvMS) и Idiopatic TNON. Для классификации TNON на основе исходных данных использовалась вс

Совпадение в аннотации оригинала
BACKGROUND AND OBJECTIVES: A subset of optic neuritis (ON) patients remains seronegative for AQP4-IgG, MOG-IgG, and CSF oligoclonal bands (O … SD (n=13), and TNON (n=39). TNON was further divided into evolving MS (evMS) and idiopathic TNON. A support vector machine (SVM) was used to classify TNON based on onset data. RESULTS: TNON patients showed less optic disc edema and radiological involvement than MOGAD and responded more poorly to steroids. However, TNON had lower relapse rates and less need for chronic …