A cascade learning system for classification of diabetes disease: Generalized Discriminant Analysis and Least Square Support Vector Machine

By Kemal Polat, Salih Günes, Ahmet Arslan. Expert systems with applications, 2008

336 citations recorded by the source corpus.

Subfields: Complementary and alternative medicine, Computer Vision and Pattern Recognition, Health Information Management

View Paper: https://www.semanticscholar.org/paper/dd0f6956fdbfe1c537fe055fc8dffbfa89bebe29

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Complementary and alternative medicine

1. Maryam Mohebbi
2. Fevzullah Temurtas
3. Seyed Kamaledin Setarehdan
4. Hasan Temurtas
5. Nejat Yumusak
6. Babak Mohammadzadeh Asl

In Computer Vision and Pattern Recognition

1. Maryam Mohebbi
2. Fevzullah Temurtas
3. Seyed Kamaledin Setarehdan
4. Hasan Temurtas
5. Nejat Yumusak
6. Babak Mohammadzadeh Asl

In Health Information Management

1. Maryam Mohebbi
2. Fevzullah Temurtas
3. Seyed Kamaledin Setarehdan
4. Hasan Temurtas
5. Nejat Yumusak
6. Babak Mohammadzadeh Asl

Citations in the corpus

• Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal (2008, methodology): “It can be considered as a kind of generalization to the well-known linear discriminant analysis (LDA) algorithm and has become a promising feature extraction scheme [19—24] in recent years.”
• A comparative study on diabetes disease diagnosis using neural networks (2009, methodology): “…this manuscript, Pima Indian diabetes dataset taken from the UCI machine learning respiratory were used (Carpenter & Markuzon, 1998; Deng & Kasabov, 2001; Kayaer & Yıldırım, 2003; Polat & Gunes, 2007; Polat et al., 2008; ftp://ftp.ics.uci.edu/pub/machine-learningdatabases (accessed: 15.02.2008)).”