Journal ArticleDOI
A comparative study on diabetes disease diagnosis using neural networks
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TLDR
A comparative pima-diabetes disease diagnosis was realized through proper interpretation of the diabetes data using a multilayer neural network structure trained by Levenberg-Marquardt (LM) algorithm and a probabilistic neuralnetwork structure used.Abstract:
Diabetes occurs when a body is unable to produce or respond properly to insulin which is needed to regulate glucose. Besides contributing to heart disease, diabetes also increases the risks of developing kidney disease, blindness, nerve damage, and blood vessel damage. Diabetes disease diagnosis via proper interpretation of the diabetes data is an important classification problem. In this study, a comparative pima-diabetes disease diagnosis was realized. For this purpose, a multilayer neural network structure which was trained by Levenberg-Marquardt (LM) algorithm and a probabilistic neural network structure were used. The results of the study were compared with the results of the pervious studies reported focusing on diabetes disease diagnosis and using the same UCI machine learning database.read more
Citations
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Review: Knowledge discovery in medicine: Current issue and future trend
TL;DR: The main idea in this paper is to describe key papers and provide some guidelines to help medical practitioners to explore previous works and identify interesting areas for future research.
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A review on non-model based diagnosis methodologies for PEM fuel cell stacks and systems
Zhixue Zheng,R. Petrone,R. Petrone,Marie-Cécile Péra,Daniel Hissel,Mohamed Becherif,Cesare Pianese,N. Yousfi Steiner,Marco Sorrentino +8 more
TL;DR: A review of non-model based methodologies applied to diagnosis of Proton Exchange Membrane Fuel Cell (PEMFC) system is presented and hybrid approaches resulting from integration of different methods are believed to be promising.
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Chest diseases diagnosis using artificial neural networks
TL;DR: A comparative chest diseases diagnosis was realized by using multilayer, probabilistic, learning vector quantization, and generalized regression neural networks to diagnose chronic obstructive pulmonary, pneumonia, asthma, tuberculosis, lung cancer diseases.
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An analytical method for diseases prediction using machine learning techniques
TL;DR: The results showed that the combination of fuzzy rule-based, CART with noise removal and clustering techniques can be effective in diseases prediction from real-world medical datasets.
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A fuzzy classification system based on Ant Colony Optimization for diabetes disease diagnosis
TL;DR: FCS-ANTMINER outperforms several famous and recent methods in classification accuracy for diabetes disease diagnosis and has new characteristics that make it different from the existing methods that have utilized the Ant Colony Optimization for classification tasks.
References
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Journal ArticleDOI
Fuzzy identification of systems and its applications to modeling and control
T. Takagi,Michio Sugeno +1 more
TL;DR: A mathematical tool to build a fuzzy model of a system where fuzzy implications and reasoning are used is presented and two applications of the method to industrial processes are discussed: a water cleaning process and a converter in a steel-making process.
Book ChapterDOI
Learning internal representations by error propagation
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
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Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations
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Learning internal representations by error propagation
TL;DR: In this paper, the problem of the generalized delta rule is discussed and the Generalized Delta Rule is applied to the simulation results of simulation results in terms of the generalized delta rule.
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Training feedforward networks with the Marquardt algorithm
TL;DR: The Marquardt algorithm for nonlinear least squares is presented and is incorporated into the backpropagation algorithm for training feedforward neural networks and is found to be much more efficient than either of the other techniques when the network contains no more than a few hundred weights.
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