Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

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

Download PDF
13 pages

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

Abstract

AbstractAs a chronic disease, diabetes mellitus has emerged as a worldwide epidemic. Providing diagnostic aid for diabetes disease by using a set of data that contains only medical information obtained without advanced medical equipment, can help numbers of people who want to discover the disease or the risk of disease at an early stage. This can possibly make a huge positive impact on a lot of peoples lives. The aim of this study is to classify diabetes disease by developing an intelligence...
Loading next page...
 
/lp/taylor-francis/accuracy-improvement-for-diabetes-disease-classification-a-case-on-a-rdhjx8W4wS
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