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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...
Medical diagnosis is an exciting are of research and many researchers have been working on the application of Artificial Intelligence techniques to develop disease recognition systems. They are analysing currently available information and also biochemical data collecting from clinical laboratories and experts for identifying pathological status of the patient. During the process of diagnosis, the...
Thyroid gland is one of the endocrine glands in the human body which produces thyroid hormone. This gland actively produces two kinds of hormone, namely thyroxine (T4) and triiodothyronine (T3). These hormones aim to produce protein, govern body metabolism, as well as to control body temperature circulation. Either excess or lack of these hormones will disturb those activities. The condition of excessive...
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...
At present, patients whose have suffered from stroke in Thailand are increasing every year. Stroke impairments relate to many functions such as sensory, motor function, communication, visual and emotional function which depend on brain's lesion. Physical examinations and assessments are important for planning the rehabilitation programs. For this reason, there are several information for medical decision...
In this paper, we introduce a method to find useful markers from sensor arrays which have massive sensing points and diagnose liver cancer based on machine learning algorithms which are neural network and fuzzy neural network. We obtain reliable results by using a learning ability and n-fold cross validation. For the verification of the proposed method, raw data of serums from 314 normal and 81 patients...
Medical diagnosis is done mostly by medical practitioner's expertise and experience. But in some cases, it may lead to wrong diagnosis and treatment. In this paper, a medical diagnosis system is proposed to predict the risk of cardiovascular diseases with high prediction accuracy. This system is built using an intelligent approach based on Principal Component Analysis (PCA) and Adaptive Neuro Fuzzy...
Needle Electromyography, in combination with nerve conduction studies, is the gold standard methodology for assessing the neurophysiologic effects of neuromuscular diseases. Muscle categorization is typically based on visual and auditory assessment of the morphology and activation patterns of its constituent motor units. A procedure which is highly dependent on the skills and level of experience of...
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...
Osteoarthritis is a degenerative joint disease, which causes the degradation of articular cartilage and subchondral bone. The disease may result in mechanical abnormalities of the joints, including weight bearing joints such as the knees and hips. In this work, we analyze gait biomechanical data using neural network models to predict the level of joint deterioration and the level of pain in participants...
Constant monitoring of a variety of physiological signals is vitally important in numerous clinical care settings. This signals are not perfect, however, and can be corrupted or lost. The loss of a signal can be devastating to the patient, as the physician may lose key information to understanding disease processes, or worse, be unaware of the patient's status in either surgery or the ICU. This study...
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...
Ears are the important organ for the hearing system. The system itself is very complicated. The clinicians attempt to determine the correct diagnosis using signs, symptoms and test results to formulate the hypothesis of the diagnosis before providing treatments. Most patients in this study have severe illness. Therefore, the clinicians decide to take the treatment by surgery rather than treating the...
In this paper we proposed an automated Artificial Neural Network (ANN) based classification system for cardiac arrhythmia disease using standard 12 lead ECG signal recordings. In this study, we are mainly interested in classifying different arrhythmia types (classes) using multilayer peceptron (MLP) model. We have used UCI ECG signal data to train and test MLP network model. For this multi class classification...
Liver biopsy is considered as mandatory for the management of patients infected with the hepatitis C virus (HCV), particularly for staging of fibrosis degree. However, due to its invasive nature and limitations of sampling error, the tendency is to substitute the liver biopsy with non-invasive method. The objective of this study is to combine the serum biomarkers and histopathological findings to...
We propose and evaluate a framework for detection of plant leaf/stem diseases. Studies show that relying on pure naked-eye observation of experts to detect such diseases can be prohibitively expensive, especially in developing countries. Providing fast, automatic, cheap and accurate image-processing-based solutions for that task can be of great realistic significance. The proposed framework is image-processing-based...
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...
This paper compared various MLP activation functions for classification problems. The most well-known (Artificial Neural Network) ANN architecture is the Multilayer Perceptron (MLP) network which is widely used for solving problems related to data classifications. Selection of the activation functions in the MLP network plays an essential role on the network performance. A lot of studies have been...
This paper presents automatic detection and localization of myocardial infarction (MI) using back propagation neural networks (BPNN) classifier with features extracted from 12 lead ECG. Detection of MI aims to classify healthy and subjects having MI. Localization is the task of specifying the infarcted region of the heart. The electrocardiogram (ECG) source used is the PTB database available on Physio-bank...
There is growing evidence that traditional Chinese medicine (TCM) plays an important role in the diagnosis and therapy of diseases. However, it is difficult to be analyzed and understood by modern science, which seriously block its development. So there is an urgent need to explore an automatic diagnosis system of traditional Chinese medicine. This paper analyzes the proper of traditional Chinese...
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