AIMC Topic: Support Vector Machine

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Breast lesion classification based on supersonic shear-wave elastography and automated lesion segmentation from B-mode ultrasound images.

Computers in biology and medicine
Supersonic shear-wave elastography (SWE) has emerged as a useful imaging modality for breast lesion assessment. Regions of interest (ROIs) were required to be specified for extracting features that characterize malignancy of lesions. Although analyse...

The identification of high potential archers based on fitness and motor ability variables: A Support Vector Machine approach.

Human movement science
Support Vector Machine (SVM) has been shown to be an effective learning algorithm for classification and prediction. However, the application of SVM for prediction and classification in specific sport has rarely been used to quantify/discriminate low...

Optimal projection method determination by Logdet Divergence and perturbed von-Neumann Divergence.

BMC systems biology
BACKGROUND: Positive semi-definiteness is a critical property in kernel methods for Support Vector Machine (SVM) by which efficient solutions can be guaranteed through convex quadratic programming. However, a lot of similarity functions in applicatio...

Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network.

Computational and mathematical methods in medicine
High-order functional connectivity networks are rich in time information that can reflect dynamic changes in functional connectivity between brain regions. Accordingly, such networks are widely used to classify brain diseases. However, traditional me...

Distinguishing three subtypes of hematopoietic cells based on gene expression profiles using a support vector machine.

Biochimica et biophysica acta. Molecular basis of disease
Hematopoiesis is a complicated process involving a series of biological sub-processes that lead to the formation of various blood components. A widely accepted model of early hematopoiesis proceeds from long-term hematopoietic stem cells (LT-HSCs) to...

Prediction of 5-year overall survival in cervical cancer patients treated with radical hysterectomy using computational intelligence methods.

BMC cancer
BACKGROUND: Computational intelligence methods, including non-linear classification algorithms, can be used in medical research and practice as a decision making tool. This study aimed to evaluate the usefulness of artificial intelligence models for ...

Hybrid Method Based on Information Gain and Support Vector Machine for Gene Selection in Cancer Classification.

Genomics, proteomics & bioinformatics
It remains a great challenge to achieve sufficient cancer classification accuracy with the entire set of genes, due to the high dimensions, small sample size, and big noise of gene expression data. We thus proposed a hybrid gene selection method, Inf...

Supervised learning for infection risk inference using pathology data.

BMC medical informatics and decision making
BACKGROUND: Antimicrobial Resistance is threatening our ability to treat common infectious diseases and overuse of antimicrobials to treat human infections in hospitals is accelerating this process. Clinical Decision Support Systems (CDSSs) have been...

Classification of cancer cells using computational analysis of dynamic morphology.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Detection of metastatic tumor cells is important for early diagnosis and staging of cancer. However, such cells are exceedingly difficult to detect from blood or biopsy samples at the disease onset. It is reported that cance...

Construction of a 26‑feature gene support vector machine classifier for smoking and non‑smoking lung adenocarcinoma sample classification.

Molecular medicine reports
The present study aimed to identify the feature genes associated with smoking in lung adenocarcinoma (LAC) samples and explore the underlying mechanism. Three gene expression datasets of LAC samples were downloaded from the Gene Expression Omnibus da...