The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Heart disease is a deadly disease that large population of people around the world suffers from. When considering death rates and large number of people who suffers from heart disease, it is revealed how important early diagnosis of heart disease. Traditional way of diagnosis is not sufficient for such an illness. Developing a medical diagnosis system based on machine learning for prediction of heart...
In this paper, the various technologies of data mining (DM) models for forecast of heart disease are discussed. Data mining plays an important role in building an intelligent model for medical systems to detect heart disease (HD) using data sets of the patients, which involves risk factor associated with heart disease. Medical practitioners can help the patients by predicting the heart disease before...
In this paper, we present a practical Electrocardiogram-based (ECG) human authentication method which is very suitable for deployment in mobile devices like smart phones. A user only needs 20 seconds to register his ECG template at the first time and about 4 seconds to authenticate afterwards. We firstly extracted heartbeats of every cardiac cycle and then separately corrected PQ segments, QRS segments...
In these days, chronic diseases are the imperative reason for death in the world. Therefore, there is a noteworthy increment in consideration being paid to individual wellness as a preventative methodology in healthcare. However, creating and building a prediction model for chronic diseases is an extraordinary change to healthcare technology on the premise of data-analysis and decision-making level...
In data mining there are several ways, approaches to predict any disease and different researches are still going on. In this survey, we have studied several algorithms (like genetic algorithm, Particle Swarm Optimization, Artificial Neural Network) which play very essential role in determining or predicting heart disease. Here we firstly describe the basic concepts of these three algorithms, and...
This paper presents the development of a Neuro-genetic model for the prediction of coronary heart diseases. The novelty of this work is feature subset selection using multi-objective genetic algorithm without sacrificing the accuracy of ANN based heart disease predictor. Subsequently, the selected feature subset is used to predict the level of angiographic coronary heart disease using neural networks...
The paper presents application of Artificial Neural networks (ANN) in classification system of medical data set, which can be helpful in diagnosis procedures. As a neural network, the perceptron feed forward architecture has been applied. The proposed ANN architecture contains one hidden layer. The elaborated models of ANN, computer simulations and results presented in this paper, have been made by...
In this study, we introduces a classification approach using Multi-Layer Perceptron (MLP)with Back-Propagation learning algorithm and a feature selection algorithm along with biomedical test values to diagnose heart disease. Clinical diagnosis is done mostly by doctor's expertise and experience. But still cases are reported of wrong diagnosis and treatment. Patients are asked to take number of tests...
The diagnosis of heart disease in most cases depends on a complex combination of clinical and pathological data. Because of this complexity, there exists a significant amount of interest among clinical professionals and researchers regarding the efficient and accurate prediction of heart disease. In this paper, we develop a heart disease predict system that can assist medical professionals in predicting...
Artificial Neural Network (ANN) are one of the recently explored advanced technologies which shows promise in the area of medical. In this paper, Radial Basis Function is used to predict the medical prescription of heart disease. This work includes the detailed information about the patient's symptoms and preprocessing was done. The trainee doctors can also use this web based tool for diagnosis and...
The medical diagnosis process can be interpreted as a decision making process, during which the physician induces the diagnosis of a new and unknown case from an available set of clinical data and from his/her clinical experience. This process can be computerized in order to present medical diagnostic procedures in a rational, objective, accurate and fast way. This paper presents a decision support...
Information generated by sensors that collect a patient's vital signals are continuous and unlimited data sequences. Traditionally, this information requires special equipment and programs to monitor them. These programs process and react to the continuous entry of data from different origins. Thus, the purpose of this study is to analyze the data produced by these biomedical devices, in this case...
A modal Based on rough set theory and BP neural network for the heart disease severity diagnosis and evaluation is proposed. According to the heart disease symptoms data quantized by Konhonen neural network, a decision table is created and reduced using the rough set theory. After reducing the decision table, the symptoms data are the input of the BP neural network, and the diagnosis data are the...
Presented a new method of recognizing and sorting of heart sound signal, using heart sound signal analysis and improved back propagation artificial Neural Network. Firstly, the heart sound signal has to be digitally filtered to eliminating the noise. The neural network adopted is improved back propagation three-layer artificial Neural Network, with an optimized BPN6-3-2 network topology. Get the characteristic...
Radial-basis-function (RBF) artificial neural network was developed to recognize the coronary heart disease patients basing on the contents of microelements in human blood. Leave-one out method was used to train the model. After training, the RBF model was used to recognize the coronary heart disease patients. Results showed that the RBF model recognized the three samples correctly, and the accuracy...
Based on the concept of granular computing, this article proposes a novel Boolean conversion (BC) method to reduce data attribute number for the purpose of improving the efficiency of learning in artificial intelligence. Data with large amount of attributes usually cause a system freezes or shuts down. The proposed method combines large amount attributes to smaller number ones by the way of Boolean...
In this paper, the application of support vector machine (SVM) approach based on the statistics-learning theory of structural risk minimization in heart disease diagnosis. Aiming at the blindness of man made choice of parameter and kernel function of SVM, a chaotic adaptive particle swarm optimization (CAPSO) method is applied to select parameters of SVM in the paper and genetic characteristics of...
Supra-ventricular Tachyarrhythmia (SVTA) called as disturbances of heart around atria and Atrioventricular (AV) node is one of the most common heart arrhythmias. Heart Rate Variability (HRV) is a pointer for classification of autonomic nervous system (ANS) and heart arrhythmias. Wavelet Packet Transform (WPT) is an efficient tool for HRV like non stationary signals. This study presents critical frequency...
A novel intelligent noninvasive diagnosis system of Coronary Artery Disease (CAD) is proposed based on Empirical Mode Decomposition (EMD)-Teager Energy Operator (TEO) and Back-Propagation (BP) neural network. The occluded arteries can produce the diastolic murmurs with high frequency energy. Firstly, the instantaneous frequency of the diastolic murmurs is estimated by EMD-TEO to identify features...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.