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Diabetes self-management relies on the blood glucose prediction as it allows taking suitable actions to prevent low or high blood glucose level. In this paper, we propose a deep learning neural network model for blood glucose prediction. The model is a sequential one using a Long- Short-Term Memory (LSTM) layer with two fully connected layers. Several experiments were carried out over data of 10 diabetic...
It is deficiency to use accuracy as a measurement to evaluate model classifying ability. This paper proposes a measurement method which uses the area under the ROC curve, or AUC value, to evaluate the performance of the model. Furthermore, applying cross validation and grid-search methods, through designed algorithms, to build an optimization of support vector machines medical prediction model. The...
A tool for discovery of gait anomalies of elderly from motion sensor data is proposed. The gait of the user is captured with the motion capture system, which consists of tags attached to the body and sensors situated in the apartment. Position of the tags is acquired by the sensors and the resulting time series of position coordinates are analyzed with dynamic time warping and machine learning algorithms...
Classification of medical data is an important task in the prediction of any disease. It even helps doctors in their diagnosis decisions. Ensemble classifier is to generate a set of classifiers instead of one classifier for the classification of a new object, hoping that the combination of answers of multiple classification results in better performance. Tuberculosis (TB) is a disease caused by bacteria...
This research aims at developing an optimal neural network based DSS, which is aimed at precise and reliable diagnosis of chronic active hepatitis (CAH) and cirrhosis (CRH). The principal component analysis neural network is designed scrupulously for classification of these diseases. The neural network is trained by eight quantified texture features, which were extracted from five different region...
Artificial Neural Networks (ANN) is currently a hot research area in medicine and it is believed that they will receive extensive application to biomedical systems in the next few years. Neural networks are ideal in recognizing diseases using scans since there is no need to provide a specific algorithm on how to identify the disease. This paper describes an algorithm to separate the lung tissue from...
Medical Diagnosis is the utmost need of an hour. Gestational Diabetics in women represents the second leading cause of yielding children born with birth defects. The ultrasound images are usually low in resolution making diagnosis difficult. Specialized tools are required to assist the medical experts to categorize and diagnose diseases to accuracy. If the anomalies in the ultrasound images are detected...
Artificial neural networks are significantly used in the field of ophthalmology for accurate disease identification which further aids in treatment planning. In this paper, an automated system based on Self-Organizing neural network (Kohonen network) is proposed for eye disease classification. Abnormal retinal images from four different classes namely non-proliferative diabetic retinopathy (NPDR),...
We propose a new ultrasonic image analysis system that can be utilized as an effective tool in classifying liver states as normal, hepatitis, or liver cirrhosis. In this system, we first define suitable settings for the ultrasonic device, then remove the inhomogeneous structures from the area of interest in the image, and then, by using the forward sequential search method, look for the useful texture...
The proper application of statistics, machine learning, and data-mining techniques in routine clinical diagnostics to classify diseases using their genetic expression profile is still a challenge. One critical issue is the overall inability of most state-of-the-art classifiers to identify out-of-class samples, i.e., samples that do not belong to any of the available classes. This paper shows a possible...
Laryngeal diseases affect many professionals who use their voices as the main working tool, such as teachers, singers, radio and TV presenters, among others. Advanced diagnosis techniques of these diseases are typically invasive, causing much discomfort to the patient. In recent years techniques of digital voice processing have been investigated to obtain non-invasive systems to aid the diagnosis...
This paper presents two artificial neural network (ANN) structures to estimate the depth of anesthesia (DOA). First, a clinical study involved on 33 patients is proposed to construct reference data and also to compare the results with BIS monitor (Aspect Medical, Vista), which represents satisfactory correlation with clinical assessments. Secondly, to extract features from electroencephalogram (EEG)...
Diabetes mellitus is a chronic metabolic disease that displays hyperglycaemia and that is strongly linked to micro and macro-vascular complications and neuropathic ones. The World Health Organization (WHO) states that there are around 171 million diabetic patients in the world, it's also estimated that this amount will double by 2030. We have performed a preliminary study on 35 volunteers, including...
Lesion segmentation is an important step in analysing dermoscopic skin lesion images. In this paper we achieve accurate lesion segmentation using a co-operative neural network-based edge detection approach coupled with a pre-processing step that enhances colour information and contrast of the images. Extensive experiments are carried out on a dataset of 100 dermoscopic images with known ground truths...
In this paper we have investigated the differences of heart rate variability (HRV) features between normal subjects and patients suffering from congestive heart failure (CHF) at several levels of NYHA scale. We analyzed 1914.4 hours of ECG of 83 patients of which 54 normal and 29 suffering from CHF with NYHA I, II, III, extracted by public databases. Following international guidelines, we computed...
Aim of the manuscript is to present an integrated web-based platform which is able to assess a person's risk to develop Cardiovascular Disease (CVD) using the Body Mass Index (BMI) as independent risk factor based on genetic and lifestyle information and in parallel to provide personalized advice in order to reduce this risk. A subject fills out a web available questionnaire regarding his/her lifestyle...
Researches during several years showed us that artificial neural networks (ANNs) have strong ability in biomedical field as well as diagnostic applications. They are capable to learn the features of exemplar sets, which is very important whilst under-test process is unknown naturally or there are some difficulties across characterization. For fast or optimally training of ANNs, extracting the most...
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