[HTML][HTML] Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records
Electronic medical records (EMRs) support the development of machine learning algorithms
for predicting disease incidence, patient response to treatment, and other healthcare events …
for predicting disease incidence, patient response to treatment, and other healthcare events …
ECG classification using wavelet packet entropy and random forests
The electrocardiogram (ECG) is one of the most important techniques for heart disease
diagnosis. Many traditional methodologies of feature extraction and classification have been …
diagnosis. Many traditional methodologies of feature extraction and classification have been …
Hybrid intelligent techniques for MRI brain images classification
This paper presents a hybrid technique for the classification of the magnetic resonance
images (MRI). The proposed hybrid technique consists of three stages, namely, feature …
images (MRI). The proposed hybrid technique consists of three stages, namely, feature …
Deep residual-dense network based on bidirectional recurrent neural network for atrial fibrillation detection
Atrial fibrillation easily leads to stroke, cerebral infarction and other complications, which will
seriously harm the life and health of patients. Traditional deep learning methods have weak …
seriously harm the life and health of patients. Traditional deep learning methods have weak …
A hybrid image enhancement based brain MRI images classification technique
The classification of brain magnetic resonance imaging (MRI) images into normal and
abnormal classes, has great potential to reduce the radiologists workload. Statistical …
abnormal classes, has great potential to reduce the radiologists workload. Statistical …
Biomedical Informatics for Computer‐Aided Decision Support Systems: A Survey
The volumes of current patient data as well as their complexity make clinical decision
making more challenging than ever for physicians and other care givers. This situation calls …
making more challenging than ever for physicians and other care givers. This situation calls …
A qualitative comparison of artificial neural networks and support vector machines in ECG arrhythmias classification
In this paper, a novel use of Kernel–Adatron (K–A) learning algorithm to aid SVM (Support
Vector Machine) for ECG arrhythmias classification is proposed. The proposed pattern …
Vector Machine) for ECG arrhythmias classification is proposed. The proposed pattern …
EEG-rhythm specific Taylor–Fourier filter bank implemented with O-splines for the detection of epilepsy using EEG signals
The neurological disorder which is associated with the abnormal electrical activity generated
from the brain causing seizures is typically termed as epilepsy. The automated detection and …
from the brain causing seizures is typically termed as epilepsy. The automated detection and …
[PDF][PDF] Automatic skin cancer images classification
M Elgamal - International Journal of Advanced Computer Science …, 2013 - Citeseer
Early detection of skin cancer has the potential to reduce mortality and morbidity. This paper
presents two hybrid techniques for the classification of the skin images to predict it if exists …
presents two hybrid techniques for the classification of the skin images to predict it if exists …
[PDF][PDF] A survey on various machine learning approaches for ECG analysis
Electrocardiogram (ECG) is a P, QRS and T wave demonstrating the electrical activity of the
heart. Feature extraction and segmentation in ECG plays a significant role in diagnosing …
heart. Feature extraction and segmentation in ECG plays a significant role in diagnosing …