Classifying Diabetes Disease Using Feedforward MLP Neural Networks

2022 ◽  
pp. 1263-1286
Author(s):  
Ahmad Al-Khasawneh ◽  
Haneen Hijazi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Chronic Diseases ◽  
University Hospital ◽  
Data Set ◽  
Common Chronic Disease ◽  
Health Practitioners ◽  
The World ◽  
Hidden Layer ◽  
Mlp Neural Networks

Diagnosing chronic diseases is about making accurate and quick decisions based on contradictory information and constantly evolving knowledge. Hence, there has been a persistent need to help health practitioners in making correct decisions. Diabetes is a common chronic disease. It is a global healthcare threat and the eighth leading cause of death in the world. Modern artificial intelligence techniques are being used in diagnosing chronic diseases including artificial neural networks. In this chapter, a feedforward multilayer-perceptron neural network has been implemented to help health practitioners in classifying diabetes. Through the work, an algorithm was proposed in purpose of determining the number of hidden layers and neurons in a MLP. Based on the algorithm, two topologies have been introduced. Both topologies exhibited good classification accuracies with a slightly higher accuracy for the MLP with only one hidden layer. The data set was obtained from King Abdullah University Hospital in Jordan.

Classifying Diabetes Disease Using Feedforward MLP Neural Networks

2019 ◽  
pp. 127-149
Author(s):  
Ahmad Al-Khasawneh ◽  
Haneen Hijazi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Chronic Diseases ◽  
University Hospital ◽  
Data Set ◽  
Common Chronic Disease ◽  
Health Practitioners ◽  
The World ◽  
Hidden Layer ◽  
Mlp Neural Networks

Diagnosing chronic diseases is about making accurate and quick decisions based on contradictory information and constantly evolving knowledge. Hence, there has been a persistent need to help health practitioners in making correct decisions. Diabetes is a common chronic disease. It is a global healthcare threat and the eighth leading cause of death in the world. Modern artificial intelligence techniques are being used in diagnosing chronic diseases including artificial neural networks. In this chapter, a feedforward multilayer-perceptron neural network has been implemented to help health practitioners in classifying diabetes. Through the work, an algorithm was proposed in purpose of determining the number of hidden layers and neurons in a MLP. Based on the algorithm, two topologies have been introduced. Both topologies exhibited good classification accuracies with a slightly higher accuracy for the MLP with only one hidden layer. The data set was obtained from King Abdullah University Hospital in Jordan.

scholarly journals Neural Network to Failure Classification in Robotic Systems

2016 ◽  
Vol 4 (1) ◽  
pp. 1 ◽  
Author(s):  
José Jair Alves Mendes Júnior ◽  
Marcelo Bissi Pires ◽  
Mário Elias Marinho Vieira ◽  
Sérgio Okida ◽  
Sergio Luiz Stevan Jr
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Robotic System ◽  
Robotic Systems ◽  
Multi Layer Perceptron ◽  
Raw Data ◽  
Failure Classification ◽  
Hidden Layer ◽  
Type Of Failure ◽  
Mlp Neural Networks

A robotic system is a reconfigurable element, and inits programming, an algorithm can be implemented in order todetect and classify failures. This is an important step to ensurethat errors in actions do not cause damage or bring risks.Considering this, a Neural Network Multi Layer Perceptron(MLP) was used, in order to classify a set of failures in robotactuators, present in a database. This purpose is to analyze ifrobotic failures could be classified by MLP. The raw data aredivided in a temporal progression manner and torque in x, y andz axes. In total, five MLP neural networks were implemented foreach type of failure classification, using two different topologies.The number of neurons in the hidden layer is in accord with thecriteria of Kolmogorov and Weka, being the latter the besttopology for such application. In comparison to an algorithm(SKIL) using the same set of data, the MLP obtained the bestperformance in any topology of classification, with hit rates in80 to 90%.

scholarly journals Construction of neural network classification of Fisher irises based on JavaScript

2019 ◽  
Author(s):  
A.М. Заяц ◽  
С.П. Хабаров
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Back Propagation ◽  
Data Set ◽  
Test Dataset ◽  
Neural Network Classification ◽  
The Neural Network ◽  
Multilayer Neural Networks ◽  
Hidden Layer

Рассматривается процедура выбора структуры и параметров нейронной сети для классификации набора данных, известного как Ирисы Фишера, который включает в себя данные о 150 экземплярах растений трех различных видов. Предложен подход к решению данной задачи без использования дополнительных программных средств и мощных нейросетевых пакетов с использованием только средств стандартного браузера ОС. Это потребовало реализации ряда процедур на JavaScript c их подгрузкой в разработанную интерфейсную HTML-страницу. Исследование большого числа различных структур многослойных нейронных сетей, обучаемых на основе алгоритма обратного распространения ошибки, позволило выбрать для тестового набора данных структуру нейронной сети всего с одним скрытым слоем из трех нейронов. Это существенно упрощает реализацию классификатора Ирисов Фишера, позволяя его оформить в виде загружаемой с сервера HTML-страницы. The procedure for selecting the structure and parameters of the neural network for the classification of a data set known as Iris Fisher, which includes data on 150 plant specimens of three different species, is considered. An approach to solving this problem without using additional software and powerful neural network packages using only the tools of the standard OS browser is proposed. This required the implementation of a number of JavaScript procedures with their loading into the developed HTML interface page. The study of a large number of different structures of multilayer neural networks, trained on the basis of the back-propagation error algorithm, made it possible to choose the structure of a neural network with only one hidden layer of three neurons for a test dataset. This greatly simplifies the implementation of the Fisher Iris classifier, allowing it to be formatted as an HTML page downloaded from the server.

Optimization Artificial Neural Network Using Artificial Bee Colony in Letter Recognition Classification

2020 ◽  
Vol 8 (4) ◽  
pp. 469
Author(s):  
I Gusti Ngurah Alit Indrawan ◽  
I Made Widiartha
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Classification Accuracy ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Letter Recognition ◽  
Bee Colony ◽  
Artificial Neural ◽  
Hidden Layer

Artificial Neural Networks or commonly abbreviated as ANN is one branch of science from the field of artificial intelligence which is often used to solve various problems in fields that involve grouping and pattern recognition. This research aims to classify Letter Recognition datasets using Artificial Neural Networks which are weighted optimally using the Artificial Bee Colony algorithm. The best classification accuracy results from this study were 92.85% using a combination of 4 hidden layers with each hidden layer containing 10 neurons.

Fourier Neural Networks and Generalized Single Hidden Layer Networks in Aircraft Engine Fault Diagnostics

2005 ◽  
Vol 128 (4) ◽  
pp. 773-782 ◽  
Author(s):  
H. S. Tan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Fourier Transforms ◽  
Aircraft Engine ◽  
Fault Classification ◽  
Noise Robustness ◽  
Fault Diagnostics ◽  
Multiple Faults ◽  
Hidden Layer

The conventional approach to neural network-based aircraft engine fault diagnostics has been mainly via multilayer feed-forward systems with sigmoidal hidden neurons trained by back propagation as well as radial basis function networks. In this paper, we explore two novel approaches to the fault-classification problem using (i) Fourier neural networks, which synthesizes the approximation capability of multidimensional Fourier transforms and gradient-descent learning, and (ii) a class of generalized single hidden layer networks (GSLN), which self-structures via Gram-Schmidt orthonormalization. Using a simulation program for the F404 engine, we generate steady-state engine parameters corresponding to a set of combined two-module deficiencies and require various neural networks to classify the multiple faults. We show that, compared to the conventional network architecture, the Fourier neural network exhibits stronger noise robustness and the GSLNs converge at a much superior speed.

scholarly journals Adaptation of the Diagnostic Artificial Neural Network Structure When New Training Examples Appear

2020 ◽  
Vol 6 (4) ◽  
pp. 120-126
Author(s):  
A. Malikov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Information Security ◽  
Classification Problem ◽  
Data Set ◽  
Neural Network Structure ◽  
Artificial Neural ◽  
Training Examples ◽  
Information Image

In this paper we can see that identified computer incidents are subject for diagnostics, during which the characteristics of information security violations are clarified (purpose, causes, consequences, etc.). To diagnose computer incidents, we can use methods of automation while collection and processing the events that occur as a result of the implementation of scenarios for information security violations. Artificial neural networks can be used to solve the classification problem of assigning diagnostic data set (information image of a computer incident) to one of the possible values of the violation characteristic. The purpose of this work is to adapt the structure of an artificial neural network that allows the accuracy diagnostics of computer incidents when new training examples appear.

scholarly journals NEURAL NETWORK ANALYSIS FOR TUMOR INVESTIGATION AND CANCER PREDICTION

2019 ◽  
Vol 2019 (02) ◽  
pp. 89-98
Author(s):  
Vijayakumar T
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Early Stage ◽  
Original Data ◽  
Data Set ◽  
Cancer Prediction ◽  
The Neural Network ◽  
Feed Forward Neural Networks ◽  
The Neural Networks ◽  
Human Nervous System

Predicting the category of tumors and the types of the cancer in its early stage remains as a very essential process to identify depth of the disease and treatment available for it. The neural network that functions similar to the human nervous system is widely utilized in the tumor investigation and the cancer prediction. The paper presents the analysis of the performance of the neural networks such as the, FNN (Feed Forward Neural Networks), RNN (Recurrent Neural Networks) and the CNN (Convolutional Neural Network) investigating the tumors and predicting the cancer. The results obtained by evaluating the neural networks on the breast cancer Wisconsin original data set shows that the CNN provides 43 % better prediction than the FNN and 25% better prediction than the RNN.

Simultaneous Approximations of Polynomials and Derivatives and Their Applications to Neural Networks

2008 ◽  
Vol 20 (11) ◽  
pp. 2757-2791 ◽  
Author(s):  
Yoshifusa Ito
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Uniform Norm ◽  
Degree Of Approximation ◽  
Probability Measures ◽  
Compact Sets ◽  
Network Parameters ◽  
Whole Space ◽  
Hidden Layer ◽  
Approximating Polynomials

We have constructed one-hidden-layer neural networks capable of approximating polynomials and their derivatives simultaneously. Generally, optimizing neural network parameters to be trained at later steps of the BP training is more difficult than optimizing those to be trained at the first step. Taking into account this fact, we suppressed the number of parameters of the former type. We measure degree of approximation in both the uniform norm on compact sets and the Lp-norm on the whole space with respect to probability measures.

Evaluation of Parameter Settings for Training Neural Networks Using Backpropagation Algorithms

2022 ◽  
pp. 202-226
Author(s):  
Leema N. ◽  
Khanna H. Nehemiah ◽  
Elgin Christo V. R. ◽  
Kannan A.
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Activation Function ◽  
Neural Network Training ◽  
Network Parameter ◽  
Network Parameters ◽  
Network Training ◽  
Rate Minimum ◽  
Hidden Layer ◽  
Function Number

Artificial neural networks (ANN) are widely used for classification, and the training algorithm commonly used is the backpropagation (BP) algorithm. The major bottleneck faced in the backpropagation neural network training is in fixing the appropriate values for network parameters. The network parameters are initial weights, biases, activation function, number of hidden layers and the number of neurons per hidden layer, number of training epochs, learning rate, minimum error, and momentum term for the classification task. The objective of this work is to investigate the performance of 12 different BP algorithms with the impact of variations in network parameter values for the neural network training. The algorithms were evaluated with different training and testing samples taken from the three benchmark clinical datasets, namely, Pima Indian Diabetes (PID), Hepatitis, and Wisconsin Breast Cancer (WBC) dataset obtained from the University of California Irvine (UCI) machine learning repository.

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