- Home
- Publications
- Publication Search
- Publication Details
Title
A soft computing approach for diabetes disease classification
Authors
Keywords
-
Journal
Health Informatics Journal
Volume -, Issue -, Pages 146045821667550
Publisher
SAGE Publications
Online
2016-11-16
DOI
10.1177/1460458216675500
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Impact of small-world network topology on the conventional artificial neural network for the diagnosis of diabetes
- (2016) Okan Erkaymaz et al. CHAOS SOLITONS & FRACTALS
- Non-insulin drugs to treat hyperglycaemia in type 1 diabetes mellitus
- (2016) Christian Seerup Frandsen et al. Lancet Diabetes & Endocrinology
- A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCA–ANFIS
- (2015) Mehrbakhsh Nilashi et al. Electronic Commerce Research and Applications
- A highly accurate firefly based algorithm for heart disease prediction
- (2015) Nguyen Cong Long et al. EXPERT SYSTEMS WITH APPLICATIONS
- Clustering- and regression-based multi-criteria collaborative filtering with incremental updates
- (2015) Mehrbakhsh Nilashi et al. INFORMATION SCIENCES
- Clustering performance comparison usingK-means and expectation maximization algorithms
- (2014) Yong Gyu Jung et al. BIOTECHNOLOGY & BIOTECHNOLOGICAL EQUIPMENT
- A novel method for studying the temporal relationship between type 2 diabetes mellitus and cancer using the electronic medical record
- (2014) Adedayo A Onitilo et al. BMC Medical Informatics and Decision Making
- A multi-criteria recommendation system using dimensionality reduction and Neuro-Fuzzy techniques
- (2014) Mehrbakhsh Nilashi et al. SOFT COMPUTING
- A hybrid intelligent model of analyzing clinical breast cancer data using clustering techniques with feature selection
- (2013) Chien-Hsing Chen APPLIED SOFT COMPUTING
- Feature generation using genetic programming with comparative partner selection for diabetes classification
- (2013) Muhammad Waqar Aslam et al. EXPERT SYSTEMS WITH APPLICATIONS
- Short-term intensive insulin therapy in type 2 diabetes mellitus: a systematic review and meta-analysis
- (2013) Caroline Kaercher Kramer et al. Lancet Diabetes & Endocrinology
- LIBSVM
- (2012) Chih-Chung Chang et al. ACM Transactions on Intelligent Systems and Technology
- A fuzzy classification system based on Ant Colony Optimization for diabetes disease diagnosis
- (2011) Mostafa Fathi Ganji et al. EXPERT SYSTEMS WITH APPLICATIONS
- A new intelligent hepatitis diagnosis system: PCA–LSSVM
- (2011) Duygu Çalişir et al. EXPERT SYSTEMS WITH APPLICATIONS
- An automatic diabetes diagnosis system based on LDA-Wavelet Support Vector Machine Classifier
- (2011) Duygu Çalişir et al. EXPERT SYSTEMS WITH APPLICATIONS
- Classification of Parkinson's disease using feature weighting method on the basis of fuzzy C-means clustering
- (2011) Kemal Polat INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
- Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes
- (2010) Wei Yu et al. BMC Medical Informatics and Decision Making
- An intelligent diagnosis system for diabetes on Linear Discriminant Analysis and Adaptive Network Based Fuzzy Inference System: LDA-ANFIS
- (2009) Esin Dogantekin et al. DIGITAL SIGNAL PROCESSING
- Beyond business failure prediction
- (2009) Wei-Wen Wu EXPERT SYSTEMS WITH APPLICATIONS
- A comparative study on diabetes disease diagnosis using neural networks
- (2008) Hasan Temurtas et al. EXPERT SYSTEMS WITH APPLICATIONS
- Design of a hybrid system for the diabetes and heart diseases
- (2007) H KAHRAMANLI et al. EXPERT SYSTEMS WITH APPLICATIONS
- A cascade learning system for classification of diabetes disease: Generalized Discriminant Analysis and Least Square Support Vector Machine
- (2006) Kemal Polat et al. EXPERT SYSTEMS WITH APPLICATIONS
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started