AIMC Topic: Support Vector Machine

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Predicting two-year survival versus non-survival after first myocardial infarction using machine learning and Swedish national register data.

BMC medical informatics and decision making
BACKGROUND: Machine learning algorithms hold potential for improved prediction of all-cause mortality in cardiovascular patients, yet have not previously been developed with high-quality population data. This study compared four popular machine learn...

Validation of accuracy of SVM-based fall detection system using real-world fall and non-fall datasets.

PloS one
Falls are a major cause of injuries and deaths in older adults. Even when no injury occurs, about half of all older adults who fall are unable to get up without assistance. The extended period of lying on the floor often leads to medical complication...

Automated Classification and Removal of EEG Artifacts With SVM and Wavelet-ICA.

IEEE journal of biomedical and health informatics
Brain electrical activity recordings by electroencephalography (EEG) are often contaminated with signal artifacts. Procedures for automated removal of EEG artifacts are frequently sought for clinical diagnostics and brain-computer interface applicati...

Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization.

Journal of healthcare engineering
With the widespread adoption of e-Healthcare and telemedicine applications, accurate, intelligent disease diagnosis systems have been profoundly coveted. In recent years, numerous individual machine learning-based classifiers have been proposed and t...

Maximum likelihood optimal and robust Support Vector Regression with lncosh loss function.

Neural networks : the official journal of the International Neural Network Society
In this paper, a novel and continuously differentiable convex loss function based on natural logarithm of hyperbolic cosine function, namely lncosh loss, is introduced to obtain Support Vector Regression (SVR) models which are optimal in the maximum ...

Automated robot-assisted surgical skill evaluation: Predictive analytics approach.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: Surgical skill assessment has predominantly been a subjective task. Recently, technological advances such as robot-assisted surgery have created great opportunities for objective surgical evaluation. In this paper, we introduce a predicti...

Turn Intent Detection For Control of a Lower Limb Prosthesis.

IEEE transactions on bio-medical engineering
OBJECTIVE: An adaptable lower limb prosthesis with variable stiffness in the transverse plane requires a control method to effect changes in real time during amputee turning. This study aimed to identify classification algorithms that can accurately ...

Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance.

Clinical science (London, England : 1979)
In the brain, enlarged perivascular spaces (PVS) relate to cerebral small vessel disease (SVD), poor cognition, inflammation and hypertension. We propose a fully automatic scheme that uses a support vector machine (SVM) to classify the burden of PVS ...

Automatic feed phase identification in multivariate bioprocess profiles by sequential binary classification.

Analytica chimica acta
In this paper, we propose a new strategy for retrospective identification of feed phases from online sensor-data enriched feed profiles of an Escherichia Coli (E. coli) fed-batch fermentation process. In contrast to conventional (static), data-driven...

Deep neural mapping support vector machines.

Neural networks : the official journal of the International Neural Network Society
The choice of kernel has an important effect on the performance of a support vector machine (SVM). The effect could be reduced by NEUROSVM, an architecture using multilayer perceptron for feature extraction and SVM for classification. In binary class...