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Accuracy Improvement for Diabetes Disease Classification: A Case on a Public Medical Dataset

Accuracy Improvement for Diabetes Disease Classification: A Case on a Public Medical Dataset This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). http://dx.doi.org/10.1016/j.fiae.2017.09.006 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Fuzzy Information and Engineering Taylor & Francis

Accuracy Improvement for Diabetes Disease Classification: A Case on a Public Medical Dataset

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Publisher
Taylor & Francis
Copyright
© 2017 Fuzzy Information and Engineering Branch of the Operations Research Society of China. Production and hosting by Elsevier B.V. All rights reserved.
ISSN
1616-8666
eISSN
1616-8658
DOI
10.1016/j.fiae.2017.09.006
Publisher site
See Article on Publisher Site

Abstract

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). http://dx.doi.org/10.1016/j.fiae.2017.09.006

Journal

Fuzzy Information and Engineering – Taylor & Francis

Published: Sep 1, 2017

Keywords: Diabetes disease disnosis; Clustering; PCA; Neural Network

References

  • Feature generation using genetic programming with comparative partner selection for diabetes classification
  • A clinical decision support framework for incremental polyps classification in virtual colonoscopy
  • A new intelligent hepatitis diagnosis system: PCACLSSVM
  • An automatic diabetes diagnosis system based on LDA-Wavelet Support Vector Machine Classifier
  • The scree test for the number of factors
  • LIBSVM: a library for support vector machines
  • A hybrid intelligent model of analyzing clinical breast cancer data using clustering techniques with feature selection
  • An efficient diagnosis system for detection of Parkinsons disease using fuzzy k-nearest neighbor approach
  • Children with diabetes mellitus: perception of their behavioural problems by parents and teachers
  • Maximum likelihood from incomplete data via the EM algorithm
  • An intelligent diagnosis system for diabetes on linear discriminant analysis and adaptive network based fuzzy inference system: LDA-ANFIS
  • Diabetes, major depression, and functional disability among US adults
  • Perceived difficulty of diabetes treatment in primary care: does it differ by patient ethnicity?
  • Impact of small-world network topology on the conventional artificial neural network for the diagnosis of diabetes
  • A fuzzy classification system based on ant colony optimization for diabetes disease diagnosis
  • An Incremental Approach to Clinical EEG Data Classification
  • Incremental eigenanalysis for classification
  • Relationships between behavioral factors and diabetic control in children and adolescents: a camp study
  • Extracting rules from multilayer perceptrons in classification problems: A clustering-based approach
  • Clustering performance comparison using K-means and expectation maximization algorithms
  • Design of a hybrid system for the diabetes and heart diseases
  • Medical diagnosis on Pima Indian diabetes using general regression neural networks
  • Diabetes mellitus in the Pima Indians: genetic and evolutionary considerations
  • Evolving fuzzy medical diagnosis of Pima Indians diabetes and of dermatological diseases
  • A highly accurate firefly based algorithm for heart disease prediction
  • Incremental learning with SVM for multimodal classification of prostatic adenocarcinoma
  • Principal component analysis in linear systems: Controllability, observability, and model reduction
  • An analytical study on behavior of clusters using k-means, em and k-means algorithm
  • A soft computing method for mesothelioma disease classification
  • A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCACANFIS
  • A soft computing approach for diabetes disease classification
  • Accuracy improvement for predicting Parkinsons disease progression
  • A knowledge-based system for breast cancer classification using fuzzy logic method
  • A multi-criteria recommendation system using dimensionality reduction and Neuro-Fuzzy techniques
  • A novel method for studying the temporal relationship between type 2 diabetes mellitus and cancer using the electronic medical record
  • FREM: fast and robust EM clustering for large data sets
  • X-means: Extending k-means with efficient estimation of the number of clusters
  • Classification of Parkinson’s disease using feature weighting method on the basis of fuzzy C-means clustering
  • A cascade learning system for classification of diabetes disease: Generalized discriminant analysis and least square support vector machine
  • Peak load forecasting using Bayesian regularization, Resilient and adaptive backpropagation learning based artificial neural networks
  • Data classification with multilayer perceptrons using a generalized error function
  • A comparative study on diabetes disease diagnosis using neural networks
  • Artificial neural network based model for forecasting of inflation in India
  • Incremental logistic regression for customizing automatic diagnostic models
  • Experimental investigation and modeling of thermal radiative properties of f-CNTs nanofluid by artificial neural network with LevenbergCMarquardt algorithm
  • A new interactive model for improving the learning performance of back propagation neural network
  • On the convergence properties of the EM algorithm
  • Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes