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The pull test (PT) is a common practice to assess the postural instability of patients with Parkinson’s disease. Postural instability is a serious issue for elderly and people with neurological disease, which can cause falls. The implementation of the PT consists in observing the user response after providing a tug to the patients’ shoulders, in order to displace the center of gravity from its neutral...
With the emergence of deep-learning algorithms, the accuracy of computer-aided supporting systems advanced., However, their adoption in the field of medicine has been limited, partially due to the challenges of generating reliable and timely results. In this research, we focused on classifying four common cutaneous diseases based on dermoscopic images using deep learning algorithms.
We propose a new variant of the Correlation-based Feature Selection (CFS) method for coping with longitudinal data – where variables are repeatedly measured across different time points. The proposed CFS variant is evaluated on ten datasets created using data from the English Longitudinal Study of Ageing (ELSA), with different age-related diseases used as the class variables to be predicted. The results...
Slowing cystic fibrosis (CF) lung disease progression is crucial to survival, but point-of-care technologies aimed at early detection — and possibly prevention — of rapid lung function decline are limited. This proof-of-principle study leverages a rich national patient registry and follow-up data on a local CF cohort to build an algorithm and prototype prognostic tool aimed at early detection of rapid...
One of the major causes of death in the world is Heart Failure. This disease affects directly the heart's pumping job. Because of this perturbation, nutriments and oxygen are not well circulated and distributed. The New York Heart Association has classified this disease into four different classes based on patient symptoms. In this paper, we are using a data mining technique, more precisely a sequential...
The unprecedented interest in big data has paved way for augmented technologies. One of the major usefulness of big data is found in the field of healthcare analytics. The healthcare data come from varied sources. Specifically EHR data provide a comprehensive view of patient's health. People are paying more attention to their health and want the best possible healthcare especially with new technologies...
In semi administered bunching is one of the vital errands and goes for gathering the information objects into classes (groups) to such an extent that the similitude of items inside bunches is high and the comparability of articles between bunches is Less. The dataset once in a while might be in blended nature that is it might comprise of both numeric and unmitigated sort of information. So two types...
Many diseases affect the knee joint, such as Chondromalica Pattelle (CP), which is the most bearing joint in the body. X-ray, MRI and arthroscopy are currently used for screening knee joint diseases. However, some of these techniques may be costly, dangerous as well as some of them being poor in functional resolution. On the other hand, researchers have shown the existence of variation in Vibroarthrography...
Brain injuries seem to be one of the most widespread diseases. Hence, the main goal of our research was to investigate feature importance in the severe brain damages dataset according to the Glasgow Outcome Scale. This scale is recognized as one of several measures used to evaluate patients' functional ability as well as their conditions after applying brain damage therapy. The current approach is...
The aim of this study is to compare some classifiers' performance related to the tuples amount. The different metrics of performance has been considered, such as: Accuracy, Mean Absolute Error (MAE), and Kappa Statistic. In this research, the different numbers of tuples are considered as well. The readmission process dataset of Diabetic patients, which has been experimented, consists of 47 features...
One of the interesting and important subjects among researchers in the field of medical and computer science is diagnosing illness by considering the features that have the most impact on recognitions. The subject discusses a new concept which is called Medical Data Mining (MDM). Indeed, data mining methods use different ways such as classification and clustering to classify diseases and their symptoms...
Heart disease is still a growing global health issue. In the health care system, limiting human experience and expertise in manual diagnosis leads to inaccurate diagnosis, and the information about various illnesses is either inadequate or lacking in accuracy as they are collected from various types of medical equipment. Since the correct prediction of a person's condition is of great importance,...
Patient monitors with arrhythmia detection will enhance the quality of living of human by aiding in prediction of diseases in much early stage. In this work we have developed an algorithm for classifying the ECG signals into normal and arrhythmic signal. Here we have detected the R peaks from denoised ECG signal with an accuracy of 97.56%. Extracted features from the signal in both time and frequency...
The ubiquitous growth of Internet of Things (IoT) and its medical applications has improved the effectiveness in remote health monitoring systems of elderly people or patients who need long-term personal care. Nowadays, chronic illnesses, such as, stroke, heart disease, diabetes, cancer, chronic respiratory diseases are major causes of death, in many parts of the world. In this paper, we propose a...
Hepatitis is one of the major health problems which can progress to chronic hepatitis and cancer. Currently, computer based diagnosis is commonly use among medical examination. The diagnosis has been examined by using the disease dataset as a reference to make the decisions. However, the dataset was incomplete because it contained many instances containing missing values. This situation can lead the...
The purpose of using Predictive Modeling for presumptive diagnosis of Type 2 Diabetes Mellitus based on symptomatic analysis is the optimization of the diagnosis phase of the disease through the process of evaluating symptomatic characteristics and daily habits, allowing the forecasting of T2DM without the need of medical exams through predictive analysis. The tool used was SAP Predictive Analytics...
A stroke occurs when the blood supply to a person's brain is interrupted or reduced. The stroke deprives person's brain of oxygen and nutrients, which can cause brain cells to die. Numerous works have been carried out for predicting various diseases by comparing the performance of predictive data mining technologies. In this work, we compare different methods with our approach for stroke prediction...
At present, the classification of brain diseases through neuroimaging data is a hot topic. Attention deficit hyperactivity disorder (ADHD) is usually diagnosed by the standard scale. However, the traditional diagnostic methods have high misdiagnosis rate and time consuming. In this paper, we discussed the classification of ADHD by using the feature subset obtained by preprocessing and feature selection...
Detecting diseases associated SNPs is the central goal of genetics and molecular biology. However, highthroughput techniques often provide long lists of disease SNPs candidates, and the identification of disease SNPs among the candidates set remains timeconsuming and expensive. In addition, contrasting to the number of SNPs involved, the available datasets (samples) generally have fairly small sample...
Thanks to the ubiquitous computing, the scale of data collection in various fields has been growing rapidly. Medicine is one of the fields that can benefit from the big data. However, it is faced with a big challenge because medical datasets are normally in high dimensions. Therefore, reducing dimensionality and finding the optimal set of features or attributes is of great importance. This paper presents...
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