An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease

By Kemal Polat, Salih Günes. Digit. Signal Process., 2007

406 citations recorded by the source corpus.

How researchers see this paper

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

Applications of neuro fuzzy systems: A brief review and future outline (2014, methodology): “[18] K. Polat, K. Gunes, An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease, Digital Signal Processing 17 (4) (2007) 702–710.”
Classification Of Diabetes Disease Using Support Vector Machine (2013, methodology): “[2] used principal component analysis and neuro fuzzy inference for diabetes data classification.”
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)).”
Predicting Diabetes Mellitus With Machine Learning Techniques (2018, methodology): “PCA (Wang and Paliwal, 2003; Polat and Günes, 2007; You et al., 2018) obtains the K vectors and unit eigenvectors by solving the characteristic equation of the correlation matrix of the observed variables.”