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Diagnosing liver disease is the challenging task for many public health physicians. In this study, we propose the framework to diagnose the hepatitis disease. For this study the adaptive rule based induction were formulated and the adaptive rule implemented in combined Robust BoxCox Transformation (RBCT) and Neural Network (NN) methods. The performance of proposed model is compared and results are...
Asthma is a lung disease caused by the inflammation and narrowing of the airways that causes recurrent attacks of breathlessness and wheezing, and often can be life-threatening. Around 15–20 million people are suffering from asthma in India[1]. This paper aims at analyzing various data mining techniques for the prediction of asthma. The observations show that the fusion approach of naive bayes and...
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...
Plant disease management is an important factor in agriculture as it causes a significant yield loss in crops. Late Blight is the most devastating disease for Potato in most of the potato growing regions in the world. For optimum use of pesticide and to minimize the yield loss, the identification of disease severity is essential. The key contribution here is an algorithm to determine the severity...
Many systems have been developed for computer analysis of the lungs in high resolution computed tomography (HRCT) scans for detection and analysis of Interstitial Lung Diseases (ILDs). This paper presents a novel approach for classification of lung tissue patterns affected with Interstitial Lung Diseases (ILDs) in high resolution computed tomography (HRCT) scans. The proposed scheme makes use of texture...
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...
Diagnosis of the thyroid function abnormalities may take much precious time of the patient. So, a computer aided diagnosis system can guide physicians in diagnosis and can save time of the patient. In this study, seven different types of neural networks were implemented in order to realize more robust and reliable networks on thyroid diagnosis. The particle swarm optimization and artificial bee colony...
This paper proposed the heart disease diagnosis system using nonlinear ARX (NARX) model. The system uses neural network for model estimation and classification of Normal and several heart diseases based on heart sounds. In classification, a spectrogram was applied to the modeled heart sounds for features extraction and selection. The features were fed to the FFNN and trained using Resilient Backpropagation...
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...
The skill of cardiac auscultatory is very important to physicians for accurate diagnosis of many heart diseases. However, it needs some training and experience to improve the skills of medical students in recognizing and distinguishing the primary symptoms of cardiac diseases based on the heart sound that heard. This paper presents a method for feature extraction and classification of heart sound...
Since ECG is huge in size sending large volume data over resource constrained wireless networks is power consuming and will reduce the energy of nodes in Body Sensor Networks (BSN). Therefore, compression of ECGs and diagnosis of diseases from compressed ECGs will play key roles in enhancing the life-time of body sensor networks. Moreover, discrimination between ventricular Tachycardia and Ventricular...
Syndrome is a unique TCM concept, which is an abstractive collection of symptoms and signs. Several modern algorithms have been applied to classify syndromes, but no satisfied results have been obtained because of the complexity of diagnosis procedure. Support vector machine (SVM) has been found to be very efficient to solve the classification problems, especially for binary classification with good...
Hepatitis C virus infection remains a public health problem within an international scope, and much efforts has been devoted to understanding the interaction within HCV and human protein complex. Among several attempts, identification of binding site and prediction of critical residue contribute significantly to the function of a protein, and can narrow the search space required by docking algorithms...
It is crucial for TCM (traditional chinese medicine) post-hepatitis cirrhosis diagnosis to accurately identify the syndrome. Meanwhile, the selection of features which are relevant to a certain TCM post-hepatitis cirrhosis syndrome not only improves the performance of the classifiers, but also provides well measure for treatment. Therefore, in this paper, we analyze the classical ART2(adaptive resonance...
Predicting Dengue Haemorrhagic Fever outbreak is obviously urgent in order to control and prevent a widespread of the fever in advance. However, the prediction of Dengue Haemorrhagic Fever outbreak needs the analysis from experts which is inconvenient and costly. An automatic prediction system should be developed. This paper proposes an automatic prediction system of Dengue Haemorrhagic-Fever outbreak...
Diabetic-retinopathy contributes to serious health problem in many parts of the world. With the motivation of the needs of the medical community system for early screening of diabetics and other diseases a computer aided diagnosis system is proposed. This work is aimed to develop an automated system to analyze the retinal images for important features of diabetic retinopathy using image processing...
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...
Proper diagnosis, classification and prediction of diabetes are essential due to the increasing prevalence of the disease and the increasing cost to control it. Appropriate discovery of knowledge from historical data for this disease would be a valuable tool for clinical researchers. The main purpose of data mining is to gain insight of the data, and extract knowledge (inter-relational patterns) from...
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