Search results for "K-Nearest Neighbours"

showing 3 items of 3 documents

Assessing Causality in normal and impaired short-term cardiovascular regulation via nonlinear prediction methods

2009

We investigated the ability of mutual nonlinear prediction methods to assess causal interactions in short-term cardiovascular variability during normal and impaired conditions. Directional interactions between heart period (RR interval of the ECG) and systolic arterial pressure (SAP) short-term variability series were quantified as the cross-predictability (CP) of one series given the other, and as the predictability improvement (PI) yielded by the inclusion of samples of one series into the prediction of the other series. Nonlinear prediction was performed through global approximation (GA), approximation with locally constant models (LA0) and approximation with locally linear models (LA1) …

Adultmedicine.medical_specialtySupine positionTime FactorsGeneral MathematicsRR intervalGlobal nonlinear predictionGeneral Physics and AstronomyNeurally-mediated syncopeBlood PressureK-nearest neighbours local nonlinear predictionCardiovascular SystemSyncopeCardiovascular Physiological PhenomenaPhysics and Astronomy (all)Engineering (all)Control theoryHeart RateNeurally mediated syncopeInternal medicinemedicinePressureHumansMathematics (all)Computer SimulationOut-of-sample predictionMathematicsModels StatisticalGeneral EngineeringLinear modelModels CardiovascularNonlinear granger causalityModels TheoreticalControl subjectsHeart rate and arterial pressure variabilityCausalityNonlinear predictionTerm (time)Case-Control StudiesSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaCardiologyAlgorithms
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Superior Performances of the Neural Network on the Masses Lesions Classification through Morphological Lesion Differences

2007

Purpose of this work is to develop an automatic classification system that could be useful for radiologists in the breast cancer investigation. The software has been designed in the framework of the MAGIC-5 collaboration. In an automatic classification system the suspicious regions with high probability to include a lesion are extracted from the image as regions of interest (ROIs). Each ROI is characterized by some features based generally on morphological lesion differences. A study in the space features representation is made and some classifiers are tested to distinguish the pathological regions from the healthy ones. The results provided in terms of sensitivity and specificity will be p…

Computer Aided DetectionSupport Vector MachineNeural NetworksK-Nearest Neighbours
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Massive Lesions Classification using Features based on Morphological Lesion Differences

2007

Purpose of this work is the development of an automatic classification system which could be useful for radiologists in the investigation of breast cancer. The software has been designed in the framework of the MAGIC-5 collaboration. In the automatic classification system the suspicious regions with high probability to include a lesion are extracted from the image as regions of interest (ROIs). Each ROI is characterized by some features based on morphological lesion differences. Some classifiers as a Feed Forward Neural Network, a K-Nearest Neighbours and a Support Vector Machine are used to distinguish the pathological records from the healthy ones. The results obtained in terms of sensiti…

Neural Networks; K-Nearest Neighbours; Support Vector Machine; Computer Aided DiagnosisSupport Vector MachineSupportVector MachineNeural NetworksComputer Aided DiagnosisK-Nearest NeighboursNeural Networks K-Nearest Neighbours Support Vector Machine Computer Aided Diagnosis.Computer Aided Diagnosis.
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