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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...
Gray mold causes a sharp drop in blueberries production, the modeling and analysis of blueberry gray mold are important for the future study of blueberry related diseases and insect pests. In the introduction, the related diseases and insect pests of blueberry were summarized. The modeling method of blueberry pests and diseases was introduced. On this basis, the blueberry gray mold was modeled. And...
The conventional approach of generating clinical opinions from general blood test (GBT) results uses the deep neural network (DNN) comprised of fully-connected layers. The large number of input neurons and output neurons result in the complex DNN structure, which causes overfitting problem. However, the dimension of the input vector and the output vector cannot be reduced arbitrarily, as all GBT results...
Gait Initiation Failure (GIF) is one of the most disabling gait disturbances seen in advanced Parkinson's disease (PD). Gait Initiation is a complex motor task that requires motor and cognitive processing to enable the correct selection, timing and scaling of movement. Failure to initiate the first step often precipitates falls and leads to significant morbidity. However, the brain mechanisms underlying...
In this article, the results of using neural network approach for solving hardly formalized task — diagnosing acute impairment of cerebral circulation, have been represented. Results of modeling neural network in the form of multilayer perceptron have been listed. The coefficients of diagnostic specificity, diagnostic sensitivity and diagnostic accuracy have been calculated.
This paper presents a system for classification of asthma based on artificial neural network. A total of 1800 Medical Reports were used for neural network training. The system was subsequently tested through the use of 1250 Medical Reports established by physicians from hospital Sarajevo. Out of the aforementioned Medical Reports, 728 were diagnoses of asthma, while 522 were healthy subjects. Out...
Mortality rate increases all over the world on a daily basis. The reason for this could be largely adduced to the increase in the number of patients with cardiovascular diseases. To worsen the case, many physicians have been known for misdiagnosis of patients reporting heart related ailment. In this paper, an intelligent system has been design which will help in effective diagnosis of the patient...
Anorexia nervosa is a quiet dangerous disease that detected in an early stage may save the patient life and flushes his veins with “passion-red blood” pumping happiness and hope with every heartbeat. A study based on back propagation neural network giving machines the ability to mimic the human function to detect a “haunted person by anorexia “ from a person obsessed by “looking “wow” as a model under...
A new methodology for improving the performance and training of neural network classifiers is presented. The main idea is based on using redundant classifiers in an ensemble in order to guarantee the best generalisation ability of the classifier. As compared to previous designs, a novel method for output combination based on weighted averaging is introduced. The proposed technique consist in considering...
People with disabilities such as those affected by IMC disease or Parkinson's disease have difficulties in operating standard joystick due to their different levels of tremors or difficulties encountered when moving their arms. The objective of this work is to design a new neural joystick suitable for each patient allowing overcoming these difficulties or incomplete or erroneous actions, in order...
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...
The traditional expert system has shortcomings of poor self-learning ability, then expert system of diagnosis of jujube diseases and insects which is based on neural networks is designed. The related symptoms of jujube diseases and insect are collected and diagnosed by expert, the conclusion of the diagnostic process is regarded as the input neurons and output neurons of neural networks. After the...
The paper introduces two neural network techniques to compare and analyze the detection level of Alzheimer's disease in a patient. The proposed module uses a Neurological Memory test named Mini Mental Status Examination (MMSE). It is authorized to be used only by neurologist, neuropsychologist and psychiatrist for determining the cognitive level. Doctors use the score of MMSE to evaluate the cognitive...
The long-term solution to the asthma epidemic is thought to be prevention, and not treatment of the established disease. The most cases of asthma begin during the first years of life, thus the early identification of young children at high risk of developing persistent symptoms of the disease throughout childhood is an important public health priority. Artificial Neural Networks have been proposed...
Parkinson's Disease (PD) is the second most common neurodegenerative action and expected to increase in the next decade with accelerating treatment costs as a consequence. This situation leads us towards the need to develop a Decision Support System for PD. In this paper we propose different methods based on evolutionary algorithms and RBF neural networks for diagnosis of PD. Three different evolutionary...
Many real world problems can be solved with Artificial Neural Networks in the areas of pattern recognition, signal processing and medical diagnosis. Most of the medical data set is seldom complete. Artificial Neural Networks require complete set of data for an accurate classification. This paper dwells on the various missing value techniques to improve the classification accuracy. The proposed system...
Medicine has always benefited from the technology. Artificial Neural Networks is currently the promising area of interest to solve medical problems. Diagnosis of diabetes is one of the most challenging problems in machine learning. This medical data set is seldom complete. Artificial neural networks require complete set of data for an accurate classification. The system explains how the pre-processing...
In this paper, we used ICA based artificial neural network (ANN) and propose a robust technique for efficient 2D echocardiography image analyzing. This project can be divided in three parts. The first part is cardiac motion estimation from sequence of echocardiography. The second part is feature extraction from motion patterns using sources, these sources is derived from independent component analyzing...
Epilepsy is a common chronic neurological disorder that is characterized by recurrent unprovoked seizures. About 50 million people worldwide have epilepsy at any one time. This paper presents an Intelligent Diagnostic System for Epilepsy using Artificial Neural Networks (ANNs) and Neuro-Fuzzy technique. In this approach the feed-forward neural network has been trained using Back propagation algorithm...
Tuberculosis infection is a serious disease which could be controlled by early diagnosis. A commonly used technique for detecting the TB bacilli is by analyzing sputum smear. Now days, image recognition systems have several applications in enormous fields. This paper uses an artificial neural network to enhance color images of Ziehl-Neelsen stained smear for the purpose of detecting TB bacilli. The...
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