Decision Support for Psychiatric Diagnosis Based on a Simple Questionnaire

1997 ◽  
Vol 36 (04/05) ◽  
pp. 349-351
Author(s):  
H. Mizuta ◽  
K. Kawachi ◽  
H. Yoshida ◽  
K. Iida ◽  
Y. Okubo ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Psychiatric Diagnosis ◽  
Psychiatric Patients ◽  
Bayesian Classifier ◽  
Neural Network Classifier ◽  
Correct Decision ◽  
Neurotic Disorders ◽  
The Neural Network

Abstract:This paper compares two classifiers: Pseudo Bayesian and Neural Network for assisting in making diagnoses of psychiatric patients based on a simple yes/no questionnaire which is provided at the outpatient’s first visit to the hospital. The classifiers categorize patients into three most commonly seen ICD classes, i.e. schizophrenic, emotional and neurotic disorders. One hundred completed questionnaires were utilized for constructing and evaluating the classifiers. Average correct decision rates were 73.3% for the Pseudo Bayesian Classifier and 77.3% for the Neural Network classifier. These rates were higher than the rate which an experienced psychiatrist achieved based on the same restricted data as the classifiers utilized. These classifiers may be effectively utilized for assisting psychiatrists in making their final diagnoses.

Credit Scoring Using Supervised and Unsupervised Neural Networks

2002 ◽  
pp. 154-166 ◽  
Author(s):  
David West ◽  
Cornelius Muchineuta
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Support ◽  
Network Model ◽  
Neural Network Model ◽  
Credit Scoring ◽  
Self Organizing Map ◽  
Subprime Lending ◽  
The Neural Network ◽  
Combined Use

Some of the concerns that plague developers of neural network decision support systems include: (a) How do I understand the underlying structure of the problem domain; (b) How can I discover unknown imperfections in the data which might detract from the generalization accuracy of the neural network model; and (c) What variables should I include to obtain the best generalization properties in the neural network model? In this paper we explore the combined use of unsupervised and supervised neural networks to address these concerns. We develop and test a credit-scoring application using a self-organizing map and a multilayered feedforward neural network. The final product is a neural network decision support system that facilitates subprime lending and is flexible and adaptive to the needs of e-commerce applications.

scholarly journals Neural Network Classifier of Oil Pollution on the Water Surface when Processing Radar Images

2020 ◽  
pp. paper42-1-paper42-12
Author(s):  
Tatiana Tatarnikova ◽  
Elena Chernetsova
Keyword(s):  
Neural Network ◽  
Water Surface ◽  
Oil Pollution ◽  
Radar Image ◽  
Neural Network Classifier ◽  
Radar Images ◽  
Dark Object ◽  
The Neural Network ◽  
Neural Classifier ◽  
Image Object

The paper proposes a solution to the problem of detecting oil pollution on a monochrome radar image. The detection of oil pollution in the image includes the solution of three tasks: detecting a dark object on the image, highlighting the main characteristics of a dark object, classifying a dark object as oil pollution or natural slick. Various characteristics of a dark object are proposed based on the contrast between the object and the background. It is proposed to use a neural network as a classifier. The input parameters of the neural network classifier of the dark image object are proposed. A technique for determining the structure of a neural classifier is presented. An algorithm for testing the selected structure of the neural network for the suitability of classifying the dark area on the image of the water surface as oil pollution or wind slick is proposed. The results of the work of the neural network classifier program for detecting abnormal objects in radar images are demonstrated.

Decision Support System for Enhancing Crop Productivity

2020 ◽  
Vol 17 (9) ◽  
pp. 4438-4441
Author(s):  
Meeradevi ◽  
Monica R. Mundada ◽  
Hrishikesh Salpekar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Support ◽  
Neural Network Model ◽  
Arima Model ◽  
Market Price ◽  
Crop Productivity ◽  
Market Prices ◽  
The People ◽  
The Neural Network

Agriculture is the important aspect for the people of India. The life of large percentage of people in India is dependent on agriculture. The farmers are facing difficulty in selling their product to the markets due to lack of knowledge on crop prices. The market prices changes drastically in time. Using neural networks market price can be predicted and made available to the farmers to decide the time to sell their product. The ARIMA model is used to forecast the prices which can help the farmers to improve their economy and also the crop yield is predicted using neural network in the proposed system. So, that the user can check the yield of the crop in the particular piece of land before sowing. The prediction using the neural network model results in deciding the time to sell the prices and what will be the production of the crop over the year.

Human Identification System Research with the Electrocardiograms Based on the Neural Network

2014 ◽  
Vol 926-930 ◽  
pp. 1104-1107
Author(s):  
Jia Lun Lin
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Identification System ◽  
Frequency Noise ◽  
Ecg Signal ◽  
Feature Subset ◽  
Neural Network Classifier ◽  
Main Research ◽  
The Neural Network ◽  
Dna Algorithm

Based on existing researches at home and abroad, an intensive study of ECG signal preprocessing, feature extraction, feature analysis and feature weight analysis was made in the Paper neural network classifier was designed to realize the ECG identification and it was optimized by GA algorithm and DNA algorithm. The main research was concluded as follows. Firstly, extracting the preprocessing and feature of ECG signal. We have analyzed the frequency of ECG signal and the noise signal included by using wavelet and wavelet threshold methods filter the low and high frequency noise in ECG signal. Secondly, analyzing weight of ECG feature and selecting the optimal feature subset. Evaluated by the accuracy rate of BP neural network classification, the optimal characteristics for identification subset is determined then. Thirdly, designing and optimizing the neural network classifier. As the BP neural network has the Problems of easily falling into local minimum and being not convergence, GA and DNA algorithm are used to optimize it.

scholarly journals INTELLECTUAL EVOLUTION SYSTEM OF SUPPORT OF DECISION-MAKING BASED ON NEURAL NETWORK AND IMMUNE APPROACHES

2017 ◽  
pp. 22-35
Author(s):  
Н.М. КОРАБЛЕВ ◽  
Д.Н. СОЛОВЬЕВ ◽  
Р.Р. МАЛЮКОВ
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Decision Support ◽  
Support System ◽  
Clonal Selection ◽  
External Environment ◽  
System Model ◽  
Immune Network ◽  
Evolution System ◽  
The Neural Network

The article considers the model of the intellectual decision support system based on the neural network, the training and evolution of which are carried out using the immune approach. The evolution of the system is considered as the task of adapting it to the conditions for changing the external environment and the properties of the decision-making object, consisting of procedures for correcting the number of neurons in the hidden layers and the parameters of the system model using   immune models for clonal selection and the immune network.

scholarly journals NEURAL NETWORK CLASSIFIER OF ENERGY FACILITIES OPERATING MODES AND ITS RECOGNITION ABILITY ASSESSMENT AT DIFFERENT NUMBER OF PRECEDENTS

2021 ◽  
pp. 45-52
Author(s):  
Yuri A. Dementiy ◽  
Aleksandr N. Maslov
Keyword(s):  
Neural Network ◽  
Electric Power Industry ◽  
Training Sample ◽  
Neural Network Classifier ◽  
Operating Modes ◽  
The Neural Network ◽  
Recognition Ability ◽  
Short Circuits ◽  
Protected Zone

Classical algorithms of relay protection construction do not use all available information base and therefore cannot provide the highest possible sensitivity with guaranteed selectivity. These algorithms, as a rule, concentrate different information, as a result of which it is partially lost. For example, the resistance relay operates with complex resistance, that is, two real parameters, although two complex variables – voltage and current – are used to calculate the complex resistance. This paper shows the solution to the problem of classification of power line operating modes using a neural network algorithm. The simplest neural network, a perceptron, is a universal classifier, since a convergence theorem has been proved for it, showing that if a classification exists, a perceptron of sufficient complexity is able to describe it. The statistical and geometrical interpretations of various algorithms are discussed. The dependence of the quality of the classifier’s work on the distribution of precedents in the training sample, on which the training is based, as well as on the structure and parameters of the neural network, is shown. The recognition ability of the neural network classifier, i.e. the ability to distinguish short circuits within the protected zone from short circuits outside the protected zone at different number of precedents in the training sample, is evaluated. The limits of applicability of such algorithms to the task of classification of object operation modes in electric power industry are shown and recommendations for their practical application are formulated. The results obtained indicate the need to develop methods for training classifiers that are based on a source of informative precedents in the form of a simulation model of the object.

scholarly journals Neural network for decision support to determine the operating mode of lined equipment

2018 ◽  
Vol 224 ◽  
pp. 04005
Author(s):  
Vitaliy Yemelyanov ◽  
Nataliya Yemelyanova ◽  
Alexey Nedelkin
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Operating Mode ◽  
Network Synthesis ◽  
Operational Mode ◽  
Iron And Steel ◽  
The Neural Network ◽  
Iron And Steel Works ◽  
Steel Works

The paper presents data on the problem of determining the operational mode of lined equipment at the iron and steel works. A neural network synthesis has been performed to determine the operational mode for lined equipment. The structure of the proposed neural network for decision support is described. The results of the modelling the neural network to determine the PM350 torpedo ladle car operational mode are presented.

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