An improved percentage rate accuracy in predicting mortality in hepatitis-c using an artificial neural network

2021 ◽  
Vol 27 (2) ◽  
Author(s):  
Daniel Matthias ◽  
I.N. Davies ◽  
O. Olumide
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hepatitis C Virus ◽  
Hepatitis C ◽  
Network Architecture ◽  
Learning Algorithm ◽  
Object Oriented Design ◽  
Intelligent Technique ◽  
Prediction Of Mortality ◽  
Artificial Neural

Background accurate prediction of mortality in Hepatitis-C (Hep C) is essential for policy action and planning. While studies have used artificial intelligent technique (e.g., artificial neural network (ANN)), their appropriateness to predicting mortality in hepatitis-c has been debated. This study presents an improved percentage rate accuracy that is capable of predicting whether a patient suffering from Hepatitis-C Virus (HCV) is likely to survive or die. The constructive research method was adopted for this study, while an Object Oriented Design Approach was adopted for the systems structural design. The Artificial Neural Network system was implemented using java programming language with many program modules and four (4) design classes namely; the Driver class that runs the application program, the Neural Network class, the Neuron and the Layer classes. The network was trained using back propagation machine learning algorithm, a learning rate of 0.8 and a learning error of 0.05. While the weights used for the training were random numbers ranging from -1.0 to +1.0. The maximum number of training sessions was set to 10000 assuming the network does not converge to the leaning error of 0.05. The result of the network showed 85% accuracy in predicting cases of the patients with positive hepatitis C virus that may survive and also 50% accuracy in predicting cases of patients with positive Hepatitis-C Virus (HCV) that may likely to die given the provided data. Neural network is a powerful classification and prediction tools that can help in predicting the outcome of Hepatitis-C virus (HCV) infections. Recommending experiment on the network architecture with a view to either increase the hidden layers or increasing the number of units in the hidden layer. Also, more extensive testing and training should be carried out to achieve the desired result.

scholarly journals Artificial Neural Network Model for Hepatitis C Stage Detection

2020 ◽  
Vol 1 (1) ◽  
pp. 11-16
Author(s):  
Dhiman Sarma ◽  
◽  
Tanni Mittra ◽  
Muntasir Hoq ◽  
Promila Haque ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Chronic Hepatitis ◽  
Hepatitis C Virus ◽  
Hepatitis C ◽  
Infected People ◽  
Egyptian Patients ◽  
Artificial Neural ◽  
Acute And Chronic Hepatitis ◽  
Prediction Evaluation

Hepatitis C is a liver disease caused by the hepatitis C virus (HCV). In 2015, WHO reports that 71 million people were living with HCV, and 1.34 million died. In 2017, 13.1 million infected people knew their diagnosis and around 5 million patients were treated. HCV can cause acute and chronic hepatitis, where 20% of chronic hepatitis progresses to final-stage chronic liver cancer. Currently, no vaccine of HCV exists, and no effective treatments are available for demolishing the progression of hepatitis C. So spotting the stages of the disease is essential for diagnostic and therapeutic management of infected patients. This paper attempts to detect stages of hepatitis C virus so that further diagnosis and medication of hepatitis patients can be prescribed. It uses a supervised artificial neural network to make a prediction. Evaluation of results is done by cross-validation using the holdout method. Hepatitis C Egyptian-patients' dataset from UCI Machine Learning Repository is used for feeding the algorithms. The research succeeds to detect the hepatitis C stages and achieves an accuracy of 97%.

scholarly journals LUNG DISORDERS DETECTION BASED ON IRISES IMAGE USING COMPUTATIONAL INTELLIGENT ART

2016 ◽  
Vol 8 (2) ◽  
Author(s):  
Anna Triwijayanti K. ◽  
Hadi Suwastio ◽  
Rini Damayanti
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Network Architecture ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Digital Camera ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Artificial Neural

Iridology as a way of revealing human organs and tissues conditions is done by iridologist by taking the image of both irises of the patients. This can be done by using a digital camera and observe each iris on the LCD display or connect the camera to a computer or a television set and observe it through the display. Research on computerized iridology has been performed before by using artificial neural network of back propagation, which is a kind of supervised learning algorithm, as the classifier [13]. Such system should be able to retain its stability while still being plastic enough to adapt to arbitrarily input patterns. Adaptive Resonance Theory (ART), another kind of artificial neural network which uses unsupervised learning algorithm, has some important traits, such as real-time learning, self-stabilizing memory in response to arbitrarily many input patterns, and fast adaptive search for best match of input-to-stored patterns [9]. That way, ART architecture is expected to be the best stable and adaptable solution in changing environment of pattern recognition. In this research, the lung disorders detection is simply designed through the steps of segmentation, extraction of color variations, transformation of lung and pleura representation area in iris image to binary form as the input of ART 1, and pattern recognition by ART 1 neural network architecture. With 32 samples and 4 nodes of output layer of ART1, the system is able to determine the existences of the four stadiums of lung disorders (acute, subacute, chronic and degenerative) in relatively short time process (approximately 1.8 to 3.2 seconds) with the accuracy of stadium recognition 91.40625% by applying the vigilance parameter value of 0.4.Keywords: iridology, lung, pleura, segmentation, ART 1 neural network

scholarly journals Investigating the Electronic Coefficients in Ne-Hg Mixtures: Comparing a Monte Carlo and Artificial Neural Network Model

2020 ◽  
Vol 55 (2) ◽  
Author(s):  
Rafid Abbas Ali ◽  
Faten Sajet Mater ◽  
Asmaa Satar Jeeiad Al-Ragehey
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Network Architecture ◽  
Vapor Concentration ◽  
Neural Network Architecture ◽  
Marquardt Algorithm ◽  
Artificial Neural ◽  
New Applications

Electron coefficients such as drift velocity, ionization coefficient, mean electron energy and Townsend energy for different concentrations of Hg 0.1%, 1%, 10% and 50% in the Ne-Hg mixture at a reduced electric field were calculated using two approaches taking into account inelastic collisions: The Monte Carlo simulation, and an artificial neural network. The effect of Hg vapor concentration on the electron coefficients showed that insignificant additions of mercury atom impurities to Neon, starting from fractions of a percent, affect the characteristics of inelastic processes and discharge, respectively. The aim of this paper is to explore the new applications of neural networks. The Levenberg-Marquardt algorithm and artificial neural network architecture employed was presented in this work to calculate the electron coefficients for different concentrations of Hg in Ne-Hg mixtures. The artificial neural network has been trained with four models (M1, M2, M3, M4), and analysis of the regression between the values of an artificial neural network and Monte Carlo simulation indicates that the M2 output provided the best perfect correlation at 100 Epochs, and the output data obtained was closest to the target data required through using the different stages of artificial neural network development starting with design, training and testing.

ARTIFICIAL NEURAL NETWORK (ANN)-BASED PREDICTIVE TOOL FOR ESTIMATING LIGHTNING DAMAGE IN COMPOSITES

2021 ◽  
Author(s):  
DEVIN NIELSEN ◽  
TYLER LOTT ◽  
SOM DUTTA ◽  
JUHYEONG LEE
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Architecture ◽  
Back Propagation ◽  
Training Dataset ◽  
Predictive Tool ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

In this study, three artificial neural network (ANN) models are developed with back propagation (BP) optimization algorithms to predict various lightning damage modes in carbon/epoxy laminates. The proposed ANN models use three input variables associated with lightning waveform parameters (i.e., the peak current amplitude, rising time, and decaying time) to predict fiber damage, matrix damage, and through-thickness damage in the composites. The data used for training and testing the networks was actual lightning damage data collected from peer-reviewed published literature. Various BP training algorithms and network architecture configurations (i.e., data splitting, the number of neurons in a hidden layer, and the number of hidden layers) have been tested to improve the performance of the neural networks. Among the various BP algorithms considered, the Bayesian regularization back propagation (BRBP) showed the overall best performance in lightning damage prediction. When using the BRBP algorithm, as expected, the greater the fraction of the collected data that is allocated to the training dataset, the better the network is trained. In addition, the optimal ANN architecture was found to have a single hidden layer with 20 neurons. The ANN models proposed in this work may prove useful in preliminary assessments of lightning damage and reduce the number of expensive experimental lightning tests.

A Framework for an Artificial-Neural-Network-Based Electronic Nose

2022 ◽  
pp. 350-374
Author(s):  
Mudassir Ismail ◽  
Ahmed Abdul Majeed ◽  
Yousif Abdullatif Albastaki
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Electronic Nose ◽  
Learning Algorithm ◽  
General Purpose ◽  
Food Spoilage ◽  
Standard Data ◽  
Semiconductor Gas Sensor ◽  
Artificial Neural

Machine odor detection has developed into an important aspect of our lives with various applications of it. From detecting food spoilage to diagnosis of diseases, it has been developed and tested in various fields and industries for specific purposes. This project, artificial-neural-network-based electronic nose (ANNeNose), is a machine-learning-based e-nose system that has been developed for detection of various types of odors for a general purpose. The system can be trained on any odor using various e-nose sensor types. It uses artificial neural network as its machine learning algorithm along with an OMX-GR semiconductor gas sensor for collecting odor data. The system was trained and tested with five different types of odors collected through a standard data collection method and then purified, which in turn had a result varying from 93% to 100% accuracy.

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