Abstract
The major task of medical science is to prevent or diagnose disease. Medical diagnosis is usually made by using some blood metrics and in addition, to be able to reach better results, one can benefit from different scientific methods. In this paper a Bayesian network method is proposed. This method is a hybrid that uses simple correlation and according to dependent variable type either simple linear regression or logistic regression for constructing a Bayesian topology. The Bayesian network is a method for representing probabilistic relationships between variables associated with an outcome of interest. To develop a Bayesian network, a structure must first be constructed. To build the topology of the Bayesian network, some alternative method can be used. One is using domain experts who usually have a good grasp of the conditional dependencies in the domain to develop the structure of the Bayesian network. Another is using structure learning algorithms, such as genetic algorithms, to construct the network topology from training data. In this paper a different construction method is proposed by using correlation analysis and one of the simple linear regression or logistic regression analyses. First, correlations of the examined variables are found. Then according to the significant correlation coefficients, the degree and direction of the interactions between these variables are established by using either simple linear regression or logistic regression. Finally the Bayesian network model is constructed by using this information. For evaluating our model, another model which does not have any relation between the input variables is also constructed. And these two models are compared by using an original thyroid data set. It is concluded that our proposed model provides a high degree of performance and good explanatory power and it may prove useful for clinicians in the medical field.
Highlights
The thyroid gland is one of the most important organs in the body and its primary role is to help regulation of the body's metabolism [13]
Simple linear regression analysis is applied if the dependent variable is ordinal-scaled and logistic regression is applied if the dependent variable is scaled nominally
Data encoding To show that considering simple correlation in forming the Bayesian network structure and one of the simple linear regression or logistic regression according to the types of the variables together gives effective results, the blood test results taken from 76 thyroid disease patients who appealed to one of the major hospitals in Turkey, had never been cured from thyroid disease and don‟t have any other systemic disease are considered
Summary
The thyroid gland is one of the most important organs in the body and its primary role is to help regulation of the body's metabolism [13]. Statistical and other quantitative methods have long been used as decisionmaking tools in medical diagnosis including thyroid disease detection. These classification methods include both parametric methods such as discriminant analysis and logistic regression and nonparametric models like k-nearest-neighbor and mathematical programming models. The simple Bayesian network where there is no connection between the input variables, and all input variables are only in connection with the output variable is used to measure the validity or the success of the model This model is verified using the 2-fold cross validation method just like the proposed model. The analyses are performed with the SPSS 16.0 and Netica 4.16 package programs
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