AIMC Topic: Support Vector Machine

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A novel approach to spinal 3-D kinematic assessment using inertial sensors: Towards effective quantitative evaluation of low back pain in clinical settings.

Computers in biology and medicine
This paper presents a novel approach for evaluating LBP in various settings. The proposed system uses cost-effective inertial sensors, in conjunction with pattern recognition techniques, for identifying sensitive classifiers towards discriminate iden...

Support vector machine-based differentiation between aggressive and chronic periodontitis using microbial profiles.

International dental journal
BACKGROUND: The existence of specific microbial profiles for different periodontal conditions is still a matter of debate. The aim of this study was to test the hypothesis that 40 bacterial species could be used to classify patients, utilising machin...

Framework for detection and localization of coronary non-calcified plaques in cardiac CTA using mean radial profiles.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: The high mortality rate associated with coronary heart disease (CHD) has driven intensive research in cardiac imaging and image analysis. The advent of computed tomography angiography (CTA) has turned non-invasive diagnosis ...

Combining sparse coding and time-domain features for heart sound classification.

Physiological measurement
OBJECTIVE: This paper builds upon work submitted as part of the 2016 PhysioNet/CinC Challenge, which used sparse coding as a feature extraction tool on audio PCG data for heart sound classification.

Prediction of lysine propionylation sites using biased SVM and incorporating four different sequence features into Chou's PseAAC.

Journal of molecular graphics & modelling
Lysine propionylation is an important and common protein acylation modification in both prokaryotes and eukaryotes. To better understand the molecular mechanism of propionylation, it is important to identify propionylated substrates and their corresp...

Automated diagnosis of prostate cancer in multi-parametric MRI based on multimodal convolutional neural networks.

Physics in medicine and biology
Automated methods for prostate cancer (PCa) diagnosis in multi-parametric magnetic resonance imaging (MP-MRIs) are critical for alleviating requirements for interpretation of radiographs while helping to improve diagnostic accuracy (Artan et al 2010 ...

Robustness of learning algorithms using hinge loss with outlier indicators.

Neural networks : the official journal of the International Neural Network Society
We propose a unified formulation of robust learning methods for classification and regression problems. In the learning methods, the hinge loss is used with outlier indicators in order to detect outliers in the observed data. To analyze the robustnes...

Machine-learning-based classification of real-time tissue elastography for hepatic fibrosis in patients with chronic hepatitis B.

Computers in biology and medicine
Hepatic fibrosis is a common middle stage of the pathological processes of chronic liver diseases. Clinical intervention during the early stages of hepatic fibrosis can slow the development of liver cirrhosis and reduce the risk of developing liver c...

Combining Low-dimensional Wavelet Features and Support Vector Machine for Arrhythmia Beat Classification.

Scientific reports
Automatic feature extraction and classification are two main tasks in abnormal ECG beat recognition. Feature extraction is an important prerequisite prior to classification since it provides the classifier with input features, and the performance of ...

Detection and diagnosis of colitis on computed tomography using deep convolutional neural networks.

Medical physics
PURPOSE: Colitis refers to inflammation of the inner lining of the colon that is frequently associated with infection and allergic reactions. In this paper, we propose deep convolutional neural networks methods for lesion-level colitis detection and ...