Journal of Statistics: Advances in Theory and ApplicationsAuthor's: Mona N. Abdel Bary, Dina H. Abdel Hady and Zenab M. EL Gamal
Pages: [67] - [91]
Received Date: August 19, 2016
Submitted by:
DOI: http://dx.doi.org/10.18642/jsata_7100121706
The health research has become increasingly reliant on statistical
modelling techniques to assess the effect of new health programs, the
impact of risk factors on disease, the effects of health behaviours,
and a host of other health concerns. Clinical researchers conduct
studies about diagnostic tests mainly for the purpose of either
estimating the diagnostic accuracy of a test according to different
patient or environmental characteristics or comparing diagnostic
accuracy of different tests according to different patient or
environmental characteristics. Studies are required to develop robust
statistical methods to analyze data from diagnostic studies and assess
the properties of available statistical methods. Multiple analytical
statistical methods are available to analyze ordinal data. These
methods can be a model based approach, such as models for cumulative
response probabilities or a non-model based approach, such as a
nonparametric method based on ranking. A common model based method
used to analyze ordinal data is an ordinal logistic regression. In
addition to statistical models, several machine learning algorithms
are also available to analyze ordinal data, such as an artificial
neural network model, a decision tree model, and a support vector
machine model. The current study compares the performance of ordinal
logistic regression model with artificial neural networks models for
prediction of chronic kidney disease. The results of the current study
show outstanding performance of artificial neural networks models for
the prediction of the level of chronic kidney disease. In addition,
the study illustrates the most affected variables are gender, surgical
operations, the blood pressure and potassium ratio. There are many
previous studies focus on demonstrated the ability of ANN models in
applications binary classifications but through the current study we
use neural networks models in multiple classifications. The results of
the current study show the following: successful of ANN models in the
process of separating and classifying accuracy rate of almost
, and the ordinal regression model
succeeded in identifying risk factors for renal failure moral
influence on the regression model.
ordinal logistics regression model, artificial neural network, medical diagnosis, chronic kidney disease.
