AIMC Topic: ROC Curve

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Optimal classification for the diagnosis of duchenne muscular dystrophy images using support vector machines.

International journal of computer assisted radiology and surgery
BACKGROUND: This study aimed to investigate the optimal support vector machines (SVM)-based classifier of duchenne muscular dystrophy (DMD) magnetic resonance imaging (MRI) images.

Prediction of psychosis using neural oscillations and machine learning in neuroleptic-naïve at-risk patients.

The world journal of biological psychiatry : the official journal of the World Federation of Societies of Biological Psychiatry
OBJECTIVES: This study investigates whether abnormal neural oscillations, which have been shown to precede the onset of frank psychosis, could be used towards the individualised prediction of psychosis in clinical high-risk patients.

Hybrid RGSA and Support Vector Machine Framework for Three-Dimensional Magnetic Resonance Brain Tumor Classification.

TheScientificWorldJournal
A novel hybrid approach for the identification of brain regions using magnetic resonance images accountable for brain tumor is presented in this paper. Classification of medical images is substantial in both clinical and research areas. Magnetic reso...

Computer-Aided Diagnosis of Parkinson's Disease Using Enhanced Probabilistic Neural Network.

Journal of medical systems
Early and accurate diagnosis of Parkinson's disease (PD) remains challenging. Neuropathological studies using brain bank specimens have estimated that a large percentages of clinical diagnoses of PD may be incorrect especially in the early stages. In...

Genome-wide prediction of prokaryotic two-component system networks using a sequence-based meta-predictor.

BMC bioinformatics
BACKGROUND: Two component systems (TCS) are signalling complexes manifested by a histidine kinase (receptor) and a response regulator (effector). They are the most abundant signalling pathways in prokaryotes and control a wide range of biological pro...

Improvement in the Prediction of Ventilator Weaning Outcomes by an Artificial Neural Network in a Medical ICU.

Respiratory care
BACKGROUND: Twenty-five to 40% of patients pass a spontaneous breathing trial (SBT) but fail to wean from mechanical ventilation. There is no single appropriate and convenient predictor or method that can help clinicians to accurately predict weaning...

Toward a General-Purpose Heterogeneous Ensemble for Pattern Classification.

Computational intelligence and neuroscience
We perform an extensive study of the performance of different classification approaches on twenty-five datasets (fourteen image datasets and eleven UCI data mining datasets). The aim is to find General-Purpose (GP) heterogeneous ensembles (requiring ...

Machine Learning methods for Quantitative Radiomic Biomarkers.

Scientific reports
Radiomics extracts and mines large number of medical imaging features quantifying tumor phenotypic characteristics. Highly accurate and reliable machine-learning approaches can drive the success of radiomic applications in clinical care. In this radi...