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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 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...
In this paper discussed the development of heart disease prediction using machine learning (in this case the Artificial Neural Network or ANN). There are 13 variables that can determine heart disease according to Miss Chaitrali paper. Prediction of a person's heart disease one year ahead is performed by studying the model heart rate data. Data is taken by using tool such as smart mirror, smart mouse,...
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...
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...
Electrocardiogram (ECG) signal has been widely used in cardiac pathology to detect heart disease. In this paper, wavelet neural network (WNN) is studied for ECG signal modeling and noise reduction. WNN combines the multi-resolution nature of wavelets and the adaptive learning ability of artificial neural networks, and is trained by a hybrid algorithm that includes the adaptive diversity learning particle...
Artificial neural networks (ANNs) have been applied to a variety of classification and learning tasks. The use of evolutionary algorithms (EA) as one of the fastest, robust and efficient global search techniques has allowed different properties of artificial neural networks to be evolved. This paper proposes the possibility of using differential evolution for determining an ANN architecture (DNNA)...
Although artificial neural network (ANN) usually reaches high classification accuracy, the obtained results sometimes may be incomprehensible. This fact is causing a serious problem in data mining applications. The rules that are derived from ANN are needed to be formed to solve this problem and various methods have been improved to extract these rules. In this study for the purpose of extracting...
The objective of this research is to implement a method for estimating the real missing data in heart disease datasets and to show how it affects the resulting knowledge. Missing data is common problem in knowledge discovery from database (KDD) processes that can lead significant error in extracted knowledge. We use hybridization of artificial neural network and rough set theory (ANNRST) to estimate...
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