Abstract
The main goal of this study is to investigate the relationship between psychosocial variables and diabetic children patients and to obtain a classifier function with which it was possible to classify the patients on the basis of assessed adherence level. The rough set theory is used to identify the most important attributes and to induce decision rules from 302 samples of Kuwaiti diabetic children patients aged 7–13 years old. To increase the efficiency of the classification process, rough sets with Boolean reasoning discretization algorithm is introduced to discretize the data, then the rough set reduction technique is applied to find all reducts of the data which contains the minimal subset of attributes that are associated with a class label for classification. Finally, the rough sets dependency rules are generated directly from all generated reducts. Rough confusion matrix is used to evaluate the performance of the predicted reducts and classes. A comparison between the obtained results using rough sets with decision tree, neural networks, and statistical discriminate analysis classifier algorithms has been made. Rough sets show a higher overall accuracy rates and generate more compact rules.
Highlights
Diabetes has become one of the most common chronic diseases among children
Several studies have shown that diabetes has a great impact on the children life
It has been shown that diabetic children are highly exposed to emotional and behavioral problems compared with normal children [1]
Summary
Diabetes has become one of the most common chronic diseases among children. Several studies have shown that diabetes has a great impact on the children life. It has been shown that diabetic children are highly exposed to emotional and behavioral problems compared with normal children [1]. Relationships and patterns within these data could provide new medical knowledge [3,4,5,6]. Analysis of medical data is often concerned with treatment of incomplete knowledge, with management of inconsistent pieces of information and with manipulation of various levels of representation of data. Existing intelligent techniques of data analysis are mainly based on quite strong assumptions (some knowledge about dependencies, probability distributions, large number of experiments), that are unable to derive conclusions from incomplete knowledge or cannot manage inconsistent pieces of information
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